{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "sns.set(color_codes=True)\n",
    "pal = sns.color_palette(\"viridis\", 10)\n",
    "sns.set_palette('muted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>index_A</th>\n",
       "      <th>index_B</th>\n",
       "      <th>index_C</th>\n",
       "      <th>index_D</th>\n",
       "      <th>Temperature_of_system1</th>\n",
       "      <th>Temperature_of_system2</th>\n",
       "      <th>Mineral_parameter1</th>\n",
       "      <th>Mineral_parameter2</th>\n",
       "      <th>Mineral_parameter3</th>\n",
       "      <th>Mineral_parameter4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-13 00:50:00</td>\n",
       "      <td>78.26</td>\n",
       "      <td>25.54</td>\n",
       "      <td>12.24</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1172.921864</td>\n",
       "      <td>813.665593</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-13 01:50:00</td>\n",
       "      <td>78.16</td>\n",
       "      <td>25.27</td>\n",
       "      <td>12.24</td>\n",
       "      <td>14.32</td>\n",
       "      <td>1095.891333</td>\n",
       "      <td>802.501333</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-13 02:50:00</td>\n",
       "      <td>78.15</td>\n",
       "      <td>26.21</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.59</td>\n",
       "      <td>854.920667</td>\n",
       "      <td>767.747500</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-13 03:50:00</td>\n",
       "      <td>78.39</td>\n",
       "      <td>25.22</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.28</td>\n",
       "      <td>854.837500</td>\n",
       "      <td>767.735333</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-13 04:50:00</td>\n",
       "      <td>79.22</td>\n",
       "      <td>24.60</td>\n",
       "      <td>12.41</td>\n",
       "      <td>13.70</td>\n",
       "      <td>843.975500</td>\n",
       "      <td>763.982667</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2022-01-22 19:50:00</td>\n",
       "      <td>79.76</td>\n",
       "      <td>22.00</td>\n",
       "      <td>11.72</td>\n",
       "      <td>18.84</td>\n",
       "      <td>1404.859000</td>\n",
       "      <td>931.219167</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2022-01-22 20:49:00</td>\n",
       "      <td>80.51</td>\n",
       "      <td>22.00</td>\n",
       "      <td>11.37</td>\n",
       "      <td>18.53</td>\n",
       "      <td>1404.868000</td>\n",
       "      <td>931.252167</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2022-01-22 21:50:00</td>\n",
       "      <td>80.16</td>\n",
       "      <td>21.78</td>\n",
       "      <td>10.85</td>\n",
       "      <td>17.90</td>\n",
       "      <td>1404.843167</td>\n",
       "      <td>931.226500</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2022-01-22 22:50:00</td>\n",
       "      <td>79.79</td>\n",
       "      <td>22.58</td>\n",
       "      <td>11.20</td>\n",
       "      <td>17.05</td>\n",
       "      <td>1404.845000</td>\n",
       "      <td>931.167833</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2022-01-22 23:50:00</td>\n",
       "      <td>80.19</td>\n",
       "      <td>21.69</td>\n",
       "      <td>10.68</td>\n",
       "      <td>17.19</td>\n",
       "      <td>1404.822833</td>\n",
       "      <td>931.238000</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Time  index_A  index_B  index_C  index_D  \\\n",
       "0   2022-01-13 00:50:00    78.26    25.54    12.24    14.14   \n",
       "1   2022-01-13 01:50:00    78.16    25.27    12.24    14.32   \n",
       "2   2022-01-13 02:50:00    78.15    26.21    12.93    14.59   \n",
       "3   2022-01-13 03:50:00    78.39    25.22    12.93    14.28   \n",
       "4   2022-01-13 04:50:00    79.22    24.60    12.41    13.70   \n",
       "..                  ...      ...      ...      ...      ...   \n",
       "233 2022-01-22 19:50:00    79.76    22.00    11.72    18.84   \n",
       "234 2022-01-22 20:49:00    80.51    22.00    11.37    18.53   \n",
       "235 2022-01-22 21:50:00    80.16    21.78    10.85    17.90   \n",
       "236 2022-01-22 22:50:00    79.79    22.58    11.20    17.05   \n",
       "237 2022-01-22 23:50:00    80.19    21.69    10.68    17.19   \n",
       "\n",
       "     Temperature_of_system1  Temperature_of_system2  Mineral_parameter1  \\\n",
       "0               1172.921864              813.665593               49.24   \n",
       "1               1095.891333              802.501333               49.24   \n",
       "2                854.920667              767.747500               49.24   \n",
       "3                854.837500              767.735333               49.24   \n",
       "4                843.975500              763.982667               49.24   \n",
       "..                      ...                     ...                 ...   \n",
       "233             1404.859000              931.219167               54.74   \n",
       "234             1404.868000              931.252167               54.74   \n",
       "235             1404.843167              931.226500               54.74   \n",
       "236             1404.845000              931.167833               54.74   \n",
       "237             1404.822833              931.238000               54.74   \n",
       "\n",
       "     Mineral_parameter2  Mineral_parameter3  Mineral_parameter4  \n",
       "0                 90.38               46.13               28.16  \n",
       "1                 90.38               46.13               28.16  \n",
       "2                 90.38               46.13               28.16  \n",
       "3                 90.38               46.13               28.16  \n",
       "4                 90.38               46.13               28.16  \n",
       "..                  ...                 ...                 ...  \n",
       "233               93.05               49.03               21.48  \n",
       "234               93.05               49.03               21.48  \n",
       "235               93.05               49.03               21.48  \n",
       "236               93.05               49.03               21.48  \n",
       "237               93.05               49.03               21.48  \n",
       "\n",
       "[238 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入数据\n",
    "\n",
    "date = pd.read_excel(\"time-hours.xlsx\")\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 238 entries, 0 to 237\n",
      "Data columns (total 11 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   Time                    238 non-null    datetime64[ns]\n",
      " 1   index_A                 238 non-null    float64       \n",
      " 2   index_B                 238 non-null    float64       \n",
      " 3   index_C                 238 non-null    float64       \n",
      " 4   index_D                 238 non-null    float64       \n",
      " 5   Temperature_of_system1  238 non-null    float64       \n",
      " 6   Temperature_of_system2  238 non-null    float64       \n",
      " 7   Mineral_parameter1      238 non-null    float64       \n",
      " 8   Mineral_parameter2      238 non-null    float64       \n",
      " 9   Mineral_parameter3      238 non-null    float64       \n",
      " 10  Mineral_parameter4      238 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(10)\n",
      "memory usage: 20.6 KB\n"
     ]
    }
   ],
   "source": [
    "date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_A</th>\n",
       "      <th>index_B</th>\n",
       "      <th>index_C</th>\n",
       "      <th>index_D</th>\n",
       "      <th>Temperature_of_system1</th>\n",
       "      <th>Temperature_of_system2</th>\n",
       "      <th>Mineral_parameter1</th>\n",
       "      <th>Mineral_parameter2</th>\n",
       "      <th>Mineral_parameter3</th>\n",
       "      <th>Mineral_parameter4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78.26</td>\n",
       "      <td>25.54</td>\n",
       "      <td>12.24</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1172.921864</td>\n",
       "      <td>813.665593</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.16</td>\n",
       "      <td>25.27</td>\n",
       "      <td>12.24</td>\n",
       "      <td>14.32</td>\n",
       "      <td>1095.891333</td>\n",
       "      <td>802.501333</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.15</td>\n",
       "      <td>26.21</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.59</td>\n",
       "      <td>854.920667</td>\n",
       "      <td>767.747500</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.39</td>\n",
       "      <td>25.22</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.28</td>\n",
       "      <td>854.837500</td>\n",
       "      <td>767.735333</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.22</td>\n",
       "      <td>24.60</td>\n",
       "      <td>12.41</td>\n",
       "      <td>13.70</td>\n",
       "      <td>843.975500</td>\n",
       "      <td>763.982667</td>\n",
       "      <td>49.24</td>\n",
       "      <td>90.38</td>\n",
       "      <td>46.13</td>\n",
       "      <td>28.16</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>79.76</td>\n",
       "      <td>22.00</td>\n",
       "      <td>11.72</td>\n",
       "      <td>18.84</td>\n",
       "      <td>1404.859000</td>\n",
       "      <td>931.219167</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>80.51</td>\n",
       "      <td>22.00</td>\n",
       "      <td>11.37</td>\n",
       "      <td>18.53</td>\n",
       "      <td>1404.868000</td>\n",
       "      <td>931.252167</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>80.16</td>\n",
       "      <td>21.78</td>\n",
       "      <td>10.85</td>\n",
       "      <td>17.90</td>\n",
       "      <td>1404.843167</td>\n",
       "      <td>931.226500</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>79.79</td>\n",
       "      <td>22.58</td>\n",
       "      <td>11.20</td>\n",
       "      <td>17.05</td>\n",
       "      <td>1404.845000</td>\n",
       "      <td>931.167833</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>80.19</td>\n",
       "      <td>21.69</td>\n",
       "      <td>10.68</td>\n",
       "      <td>17.19</td>\n",
       "      <td>1404.822833</td>\n",
       "      <td>931.238000</td>\n",
       "      <td>54.74</td>\n",
       "      <td>93.05</td>\n",
       "      <td>49.03</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index_A  index_B  index_C  index_D  Temperature_of_system1  \\\n",
       "0      78.26    25.54    12.24    14.14             1172.921864   \n",
       "1      78.16    25.27    12.24    14.32             1095.891333   \n",
       "2      78.15    26.21    12.93    14.59              854.920667   \n",
       "3      78.39    25.22    12.93    14.28              854.837500   \n",
       "4      79.22    24.60    12.41    13.70              843.975500   \n",
       "..       ...      ...      ...      ...                     ...   \n",
       "233    79.76    22.00    11.72    18.84             1404.859000   \n",
       "234    80.51    22.00    11.37    18.53             1404.868000   \n",
       "235    80.16    21.78    10.85    17.90             1404.843167   \n",
       "236    79.79    22.58    11.20    17.05             1404.845000   \n",
       "237    80.19    21.69    10.68    17.19             1404.822833   \n",
       "\n",
       "     Temperature_of_system2  Mineral_parameter1  Mineral_parameter2  \\\n",
       "0                813.665593               49.24               90.38   \n",
       "1                802.501333               49.24               90.38   \n",
       "2                767.747500               49.24               90.38   \n",
       "3                767.735333               49.24               90.38   \n",
       "4                763.982667               49.24               90.38   \n",
       "..                      ...                 ...                 ...   \n",
       "233              931.219167               54.74               93.05   \n",
       "234              931.252167               54.74               93.05   \n",
       "235              931.226500               54.74               93.05   \n",
       "236              931.167833               54.74               93.05   \n",
       "237              931.238000               54.74               93.05   \n",
       "\n",
       "     Mineral_parameter3  Mineral_parameter4  \n",
       "0                 46.13               28.16  \n",
       "1                 46.13               28.16  \n",
       "2                 46.13               28.16  \n",
       "3                 46.13               28.16  \n",
       "4                 46.13               28.16  \n",
       "..                  ...                 ...  \n",
       "233               49.03               21.48  \n",
       "234               49.03               21.48  \n",
       "235               49.03               21.48  \n",
       "236               49.03               21.48  \n",
       "237               49.03               21.48  \n",
       "\n",
       "[238 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = [\"index_A\",\"index_B\",\"index_C\",\"index_D\",\"Temperature_of_system1\",\t\"Temperature_of_system2\",\t\"Mineral_parameter1\",\t\"Mineral_parameter2\",\t\"Mineral_parameter3\",\t\"Mineral_parameter4\"]\n",
    "# sns.heatmap(name)\n",
    "date[name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zePKeEAgEsHX1Qk5iwfYqbXsj/uoJ7Pr0LfzzxdvQqFWo3nmgPlZsKUX81ZP4Z9pg3N27DrV7jIBQJEJ55mknQZJMafC3kyRTwsve0iDOzVYCkVBgcLPq2bga7jao7GqNnbfM/5EOAPBykCIhW2HQNwnZCvg4GE+JTpEp8cG229h/Lw145t7D0Qfp2H37SZ+42Urg7SBF5KMBubmS58qQm5kOF+8n7xlnL1/IMtKQL8sudJ8e/5uB7uM+h7W9o0F5UlQ4bByccf/iSSz78B0sntAfZ7avg42D8c0OolfFAXg55eXlhQsXLpR1NYxs3boVb775JjZseP50nvIsMTUbOp353wV/VXn5BRezUumTD+nHv8vlcqN4pUqJqtWqY+2GrTh2+iKGDh+Fjz+ciMzMguc358yagY6du6JuvWAT1L50KeQFfSOxfJLhf/y7UmHcN7FRD7F78yoMn/TFC4+7cO0/mLN4PcJvX8e2dYVP9S+P1MqCNoslTy5iH/+uURkPgNQKOUQWhhfGIoklNErDWJ1Wi0ubFiMnJQ41Ow8w2JZ45zJykuNQrW3PEmmDKUgthFCotQZlCrUW0pdMc+1brwJaBDpj6cmCAZSdpRidqrvh+MN0DPjzMpaficFH7Sujipt5PyIjtRBB+Uz/KNVaWL6kf3rW8URTfyesOBOjL3v65oW50Cjk+kz0YyILidF7SK2QQyASQygSPxVnqY9TK+UQPbNOx9PvL51OB/+mXdBl1lp0+GIFBBDg+lbDqbQeNRuh2zebUbv3+7iwah5ykmJLrJ2lQSISQqUx/JxWanSQiAz/dixFQqi1Ojyd2FZpdLB4FGdnKUK/ep5YeSEemtfkY99SLDR+X2m0kIiNH87IV2mhLELDna0tMDnUH8cfZugz4uZKpSiov4Xkyef549/VysJv4Nk5uxZaLpdlIzcrHYkP72LIzEUYNH0h4h/cxvENK0q41mZMICj7HzPFAXg5FRsbi6CgIGzZsgXjxo3D6NGjERwcjN69e+PevXsAAJVKhWnTpqFBgwbo3r07IiIi9PurVCp88803aNmyJUJDQ7F0acEHclJSEkJCQnDkyBEAwK5du9C6dWvIZLKX1unWrVvIycnB5MmTsXv3buTm5pZ8w6nEfD1zOlo2aYCWTRqg9xtdAQCKpwaUcnnBB5W1lfGUs2/nzkbNWrVRtVp1WFpaYuh7I2FjbYPDB/dj145tiIuLxXsj3jdNQ0rBHz/PxcjerTGyd2t8PLJgsS/lUx/Ojwfels9MhVYq5Fg87wsMGfMR7B1ffBdcIrGEl18AOvUYgAunjpRsA0rYhb9+xpYpfbBlSh/smT0KAKBRPZmy+PjCRSwxfj5QJLE0iAUAjVIB8VM3NJR5MhxfOgMp4TfQdsI3kNo7GcRHnNmHig3bmsXU88fkKi0sRYYf/pZiIfJVhWeuLUQCfNA2EG/W8cCUbbcQk1nwN6bUanE1LhsnH2ZAo9XhXFQmrsRmoWmAU6HHMRdytdbomVuJWAj5C/pnQit/hAW5Y+ruu4jLMr75ZU5EEqnx+0KlNHoPiSWW0GnU0Go0T8UpIJJY6bdrC3l/iSylkGen48qGhajSvh8spNawcnRFULd3EXP+gMG/LRJbQCgSo2LjjrDz9EPynfJ3c/9pSo0WFkLD95ZEJIBCYzzwFAsFeDrU4qm4wQ28cOBeGpJkhv1nzhRqrf4Gw2MSkfHNwKKq5maDaZ0q41p8DlZeiCuJKprc/t9/xI8j3sCPI97A8o+GATAcbKse3VS2eMWF00QWEui0WoT2HwFLKxvYObuhUVg/3L9wvOQqT/9ZHICbgQMHDqBPnz44e/Ysatasifnz5wMAVq9ejZs3b+LgwYP44YcfcPLkSf0+y5Ytw9WrV7Ft2zZs2LABO3bswK5du+Dh4YEpU6bg66+/RlJSEmbPno1Zs2bB1vbl04i3bNmCN998E56enqhbty527nz+MzVU9j77YjqOn7mI42cu4sTZS3B1c0NkxEP99siIh3B1c4Odvb3RvklJiVCrDVcgFovFsLCQYM+uHQi/dxftQpuidYtG2LFtC/7ZvQv9+7xZ6m0qKe+O+wRLNx/B0s1HsGzLUTg6uyIhJlK/PSE2Co7OrrCxtTPYL+L+bSTFx2DJvC8wul87fDKqYAr61LEDcfrIXjy4exOfju5vcDGtUilhXc6n6Ye8NU6/EFrv+ZshtXdG9lNZspykWEjtnSGxNm6Hg2dF5CQ/iVXm50KenaGfli5LTcSBBZMgFInR9n/z9aujP6bVqJFw8zz8GrQqpdaVjqiMfPg4Gl7Q+TpZITrDOINkLxVjQY8g+DhKMX7jTUSkPYmJyZDDxtJwSrBQIDCbxcaeJyYjH14OhoNNH0ep/sbD0+wsxZgdVh1eDlJ8uO0Wosw8CwcAdp5+kKXEGcy4kiXHwu6pRzsAwMbNGwKhCLKUOMO4R1PS7Tz9DN5fOp0OspQ42Hn4QZGdAZ1GDZ3myblaIBIDAiEEQiFu7fwTt3b9afDvadUqWEjL9+yKxGwF3J9ZzNDDVmL0CEKyTAmNVmewsJ+HrQRJ2Qo4WYlRycUK3Wu6YW5YFcwNqwILkRCjmvigfRXznUIclyVHhWem4lewt0Rc1qs/ntEq0AmTQv2x5VoS1l1OKKkqmlyHoRMxcdl2TFy2Hf/7bQdsHJ2RlvDUDJr4GNg4OkP6ip/Dzl4F70FF3pNkk06rxX9wEiWVAg7AzUDVqlXRrl07SCQSdO7cGdHR0QCA/fv3Y8iQIXB0dESlSpXQs+eT6Zvbtm3DhAkT4OzsDA8PDwwbNgxbt24FAPTr1w8eHh7o27cv2rdvjxYtWry0DkqlErt27UKvXr0AAL1798Zff/1VCq2l0tKtew8s+3UxUlNTkJqagt+WLkG37j0KjW0Z2hrrVq/EwwfhUKvV2LB+DdLSUtG0eQv8vOQ3HDt9EUdOnMORE+fQ/c1e6Nw1DOs3bTNtg0pQ83ZdsW3dCmSmpyEzPQ3b1q1A83ZdjeKq1QrGb1uPYfGGg1i84SDm/roRADDrlzVo2roTfCpWgjw/D3+vWw6NRo2oB/ewd9t6tO5sPjcnAMC/UVvc2rsO+dkZyM/OwK196+HfqO1zYyPOHkB61D2olQpc2/47nCtWhZ27N1TyPBxdNBVuVWqj+fCphWa4M+MioNNpC30uvDy7FpcNhUaLAQ28IBYKEOLrgIZ+Djh4L80gTigAvupaDVlyNT76+7bBs84AsP9OCiq72aBTDTcIADSq6Ii63vY4/sD468zMyY2EHCjVWvStVwFioQDBPvao7+OAI+HG/TO1U2XkKNSYuusuMvPVzzmieXGtVAsiCwnuH9gArVqF5DsXkXznInzqtzaIE1tK4VWnGW7v+hOq/FzIUuLx4Nh2+DYseL9512+t31erVuHegb8gkkjhWqkW7Dz9YO3iiRvblkOtVEAhy8bt3SvhVac5hCIxnANqIPLUHmTG3IdWo8GDo9ugkGXBo2bjMuiRorufmgeVRoeOVV0gEhQ8x13DwxYXYg2f4VVqdLgSn4M3arpDKhbCzcYCbSo742xMNjLy1fhgxz18suu+/kel0eLXM7E4cN9831t3knOh1GjRvaY7REIBalewRR0v+0K/3u9F6nrZYXCIN348HoWjZn6ueVbNFh1w+u/VyM1MR25mOk5vW4OaLTq88nHcfAJQoVJ1HFn7K5T5eZBlpOHcrg2o0bRNKdTaTJX1AmxmvAgbvwfcDDg7P7lbKxKJoH309SNpaWlwc3uSTfLy8kJaWsHFTUJCAsaMGaP/7mGtVgsfHx8ABQuz9OzZE59++inCwsKKVIfDhw8jIyMD/fv3BwBoNBpkZGTg+vXrqF279r9vJJW6kaPHQS6XY+BbvaBWqdCxc1e8P2a8fnu/nt3QOaw7hg0fhZGjxkKnAyaMHQmZTIYqVari5yXL4eJS+LNS5q7nwBFQKuT4csJgqNUqNGnVAb0GjdJv/3R0fzRr3Rnd33r3hcexlErxwfTvsfrXBRjbvyPsHZ3RY8AwNG9rPJgvz2p2GQiNUoH98ydAq1bDt34r1Oo6SL/9nzlj4NegNYI69oNnjQao3W0ITv85DwpZFlwDg9B0aMFikNGXjiE3LRHRFzMQc/GYfn87d290mPIjACA3LQmWdo7lfmGoZ6m1Ony+8w7GtwpA32AvZOQpMe/gA0Rn5KNWBTvM6lYNI9Zdg6+TFWp42kKh1mLjew30+yvVWvT7/RIi0/Px+Y47eK+pH0Y1r4j0XCW+ORCOh2nm/V3Faq0OX+29j/ebV0TPOp7IyFfhh6MRiM2UI8jDFl90roLxm27Cx1GKau4F/bNqcD39/iqNFkNWXy27BvxLQrEFmoyYjmubFyP88BZY2jkheMAk2Hn6Ie3hTZxZNgNtPvoF1k5uqNNnNK5vXYqDc0YBAgH8m3WBf9OCb1iw9/RD/YEf4Ob2FcjLTIVDBX80Hv4lhI++07jJiGm4uW05DswcBoHYAhVqNUZQ2DsAAM+ajRDU7V1cWDUfqrwcOHhXQrP3Z8HS1njWU3mi0QGLT8WgXz1PtK/ijGyFBqsuxiMxR4lAFyuMbuqLrw8+REa+GhuuJqJPHQ982SEQWh1wMjITpyIzy7oJpUaj1WHBkQi8E+KNrjXckJWvxtLT0YjPVqCqmzUmhwbgs933XrpuQrcgd4gEAkx86uv+AGDr9STsvWv8PermpFnPwVArFVj5xZiCRQmbtEbzXu/ot//+6QjUaNoWTd4Y8IKjFOg5eSaOrFmC3z4aCq1ajepN26B573deuh/Rywh0/8UVqcxAbGwsOnbsiFmzZmH79u34448/AABnz57F1KlTsX//fgwcOBB9+/ZFjx49AAA//PADEhMTMXfuXLRt2xYLFy5EzZo1AQBZWVmQy+Xw8PBAbm4uunXrhuDgYDx48ACbN2+GWPziezHvv/8+6tevb5BlX7BgASwsLDBz5switckqeNyrd8R/QPLpn8q6CuXazWeyHvTErvDXY2Xf0nLu4euV2SlJVhLzuuFhalU87V4e9B+lUPOy8Xmy81+f581LQ/NAh5cH/YeNaFzx5UHliFXoV2VdBeQf/bKsq1As5pu7J3Tt2hW///47UlNTER0djS1bnnxvcVhYGBYuXIjs7Gzk5ORg0qRJWLx4MQDgu+++Q1BQEL799ltYWFhg+fIXr9CcmpqKEydOoEePHnBzc9P/9OzZEzt37izSAm5ERERERET/dRyAm7EBAwagWbNm6NKlC9577z20bNlSv23s2LHw8fFBWFgY2rVrBxcXF3z00Ue4cuUKtmzZgs8++wxCoRDTpk3DkiVLDFZQf9a2bdtQt25duLu7G5Q3bNgQ9vb22LFjR6m1kYiIiIiI6HXBKehkMpyCXjhOQX8xTkF/Pk5BfzFOQX8+TkF/MU5Bfz5OQX8+TkF/MU5BfzGzm4LepmiPoJam/MNflHUVioUZcCIiIiIiIiIT4CroBACYPHkyDh8+XOi2MWPGYMSIESauERERERERlUtm/DVgZY0DcAJQsDAbERERERERlR7euiAiIiIiIiIyAWbAiYiIiIiIqOgEgrKugdliBpyIiIiIiIjIBDgAJyIiIiIiIjIBTkEnIiIiIiKiouMq6MXGniMiIiIiIiIyAWbAiYiIiIiIqOi4CFuxMQNOREREREREZAIcgBMRERERERGZAKegExERERERUdFxEbZiY88RERERERERmQAz4ERERERERFR0XISt2JgBJyIiIiIiIjIBDsCJiIiIiIiITIBT0ImIiIiIiKjouAhbsbHniIiIiIiIiEyAGXAiIiIiIiIqOi7CVmzMgBMRERERERGZAAfgRERERERERCbAKehERERERERUdFyErdjYc0REREREREQmwAw4ERERERERFR0XYSs2DsDJZJJP/1TWVSiX3JtOKOsqlGt+HbuXdRXKrab1KpR1Fcq1AQ3ZP88jV2vLugrlWmSGsqyrUG5JLcq6BuVXNTf7sq5CuXbobkZZV6FcG9G4YllXgUyEU9CJiIiIiIiITIAZcCIiIiIiIio6LsJWbOw5IiIiIiIiIhNgBpyIiIiIiIiKjhnwYmPPEREREREREZkAB+BEREREREREJsAp6ERERERERFR0/B7wYmMGnIiIiIiIiMgEOAAnIiIiIiIiMgFOQSciIiIiIqKi4yroxcaeIyIiIiIiIjIBZsCJiIiIiIio6LgIW7ExA05ERERERERkAhyAExEREREREZkAp6ATERERERFR0XERtmJjzxERERERERGZADPgREREREREVHRmtAjbxYsXMX36dMTExCA4OBjffPMN3N3dDWKCg4MNXisUCjRp0gQrVqzA2bNnMXToUFhaWuq3r1mzBkFBQcWqDzPgRERERERE9NqRy+WYMGECxo4di3PnzsHf3x9z5swxirt8+bL+Z+fOnXB2dsbkyZMBAHfv3kWPHj0MYoo7+AaYASciIiIiIqLX0OnTp+Hq6orOnTsDACZNmoTmzZtDJpPB1ta20H2mTZuGgQMHolatWgAKBuDVq1cvsToxA05ERERERERFJhAIyvynKKKiohAYGKh/bW9vD3t7e0RFRRUaf+rUKTx48ADvvfeevuzu3bs4dOgQWrZsiU6dOmHr1q3/qu+YASciIiIiIiKzsnDhQvz8888GZePGjcP48eP1r/Py8iCVSg1ipFIp8vPzCz3mihUrMGzYMEgkEn2Zq6sr2rRpgx49euD27dsYOXIkfH19ERISUqx6cwBORERERERERVbUDHRpGj9+vMFguzBWVlZQKpUGZXK5HDY2NkaxaWlpOH/+PL777juD8iVLluh/r1evHrp3745Dhw4VewDOKehERERERET02gkICEBkZKT+dU5ODrKysuDn52cUe/z4cTRq1Aj29vb6suzsbHzzzTdQqVT6MpVKZZAhf1UcgBMREREREdFrp0mTJkhISMDu3buhVCrxww8/oFWrVoVmwK9fv466desalNna2mLv3r1YsWIFNBoNLly4gN27dyMsLKzYdeIAnIiIiIiIiIpOUA5+ikAqlWLx4sVYunQpGjdujMjISHz11VcACr77+8KFC/rYuLg4uLq6GuwvFAqxaNEiHD16FCEhIfj0008xc+ZMVKlSpchd9Sw+A05ERERERESvpbp16+Lvv/82Kr98+bLB66ef9X5a9erVsXbt2hKrDwfgREREREREVGTlYRE2c8UBOL021CoVFsyfi317d0Or0aJDpy6Y8slnsLAofJGElX8sx4b1a5CTk4OAgED8b/JHqFe/gUGMTqfDhDEj4eLqiukz55iiGWUu0NcVx1dNQf3es5CUllPW1Sl1lT1s8FXPmqjuZYfUHCUW/HMPe68nvXCfr3oFwd/VBkOWnteXDWjii3dbVoSrrSVuxmXj6x13cCfB/PvPy94S7zbygZ+jFFlyNTZeTcSFmKznxktEAnzcNhD77qbibPSTuDaVndGpuhscpGKk5aqw7UYSzr/gOOZCo1bjwJrFuH36CHRaLWo0CUWHIWMhElu8cL+zuzci4vol9P/4yXklOfoh9q9ahKTI+7Cyc0DDzr0Q0rFHKbeg9GjUahxdtwR3zx6FTqtF1Uat0GbQmJf2zcU9mxB18xJ6ffi1vuzuuaPYs2QuxE+dz+t37o1mPYeUWv3/La1Gjet//4bYy8eg02rhXa8l6vYaCeFz2h9xcg/uHtwIZW42XAKCEPzWeFg7uQEA8jPTcPmvn5AWcRtiqTWqtuuDSi27GR3j/uGtSL53Bc1HzdCXZcVF4NrfvyEr7gFEFpbwqtMUtboPhUhiWToNL4bsxGhc3fgLsuIiILVzRFC3d+BVt7lRnEqeh6sbfkHy3UsQiMTwb9oJ1TsP1A8E4q+dxq1dKyHPTIG9VwDq9R0Ley9/AEBeehKub12GtIjbEAiFcK9aD7V6jIClrb3Rv1PeadRqHF+/BPfOFby3qjRqhdC3X/7euvTPJkTfuoQek7822qbT6bDt+89h4+CMDu99WFpVL1U+DlK819QH/k5WyMxXY92leJyLfvHn1dSOlbHndgpOR2bqy/2cpHinoQ/8nKTIVWrw9/UkHAlPN0EL6L+Cz4DTa+PXxT/jzu2b2LR1FzZu3YE7t2/hz9+XFxp74tgR/LV2NZYuX4XDx8+iU5cw/G/CaKifWuEQAFav/B1nz5wyRfXLhT4d6+PgislwdjBemOJ1ZCES4Nd36+PwnRQ0mn4IM7fdxpy+teDtJH3uPh1reaBPQx+Dsk61PfBRWFVM23ILjWYcwp5rifhzZEO42BZ/hczyQCQUYFJoAK7GZWPs5ptYfTEOwxv7wNWm8Iu8ik5W+Kx9JVRyNfz7qetlh951PPHrqWiM3nQTG64kYEQTX/i9oJ/NxfHNfyIx4j5GzluOEd/8hsTIcJzZueG58fK8XOxb+QsOrV1qUK7Iy8Vf8z+Du18gJvyyEX0/mImzuzbg8qFdpd2EUnP675VIjgzHO18vw5CvlyI5KhwXdm98brwiLxeHVy/Csb+WGW1LjgxHzRYdMe7Xbfqf8jz4BoDb/6xFRkw42n+yGO0/XoSsuAe4d2hLobFJdy7h1p5VaPLeVITNXANrZ3dcWvejfvu5P7+BtYsnus5ag8ZDP8OdvWuR+vCmfrsqPxdXt/yKG9tXGBxXq1bh1LIZcKtcC11nrkbrSd8hI/oebu5eVTqNLgatWoWzv30Fj6CG6Dp7LWr3GoVL635EXrrxjdCrGxdBo1ai45cr0HL8N4i9dAyxF48AAHKSYnBp7Xeo2+d9dJ29Dh41GuDcn3Oh02oBAOf//AaWdo7o+OUKtPtkMVTyXFzd+LPRv2EOzm5bieSocAyatQyDZi1FSlQ4Lu558Xvr6JpFOLHB+L312OW9mxBz8/Jzt5d3IqEAU9oG4HJsNoavv44/zsVidHM/uNoU/jkc4GyFaZ2roIqb4eeVo5UYX3aqgpMRGRj51w0sPhmNoY18UNHJyhTNoP8IDsDLsfj4+Ff+frlFixbhk08+KZX6DB48GLVr10ZwcDDq1auHVq1aYenSpS/f0UR2bN+Kd98bCSdnZ7i6uWPEqNHYvGF9obGRERHQarXQajUAAIFACKnUcDBw6+YN/L1lEzp06lLqdS8PxgwIxRejwzBj0Y6yrorJNK7kDBtLMX47GgG1Vofj91Jx/F4q+jbyKTTey1GKj7pWxfozMQblnWt7YOuFeJx5kA6NVod1Z2KQJlOgc21PUzSj1NRwt4HUQojdt1Og0QHXE2S4npCD0ErORrF+TlJ82CYAJyMykJpr+H2bjlYW2H07BRHp+QCAawk5iM+Wo5KLtUnaUZquH9+HZt37w9reEbZOLmjecxAuHXz+e2jdnCnIy8pAcLvuBuWx929CJc9Hu7dHQSyRwNW7Iuq3fwNXDpvvAPzWif1o2O2tgr5xdEGTNwfi6qGdz43fNO9j5GVnoE5b48xuctR9eARULc3qlrjocwdRrX1fWNo6QOrgjGod+yPi1O7nxvqGtIWjdyBEEkvU6j4UqQ+uQ5YSj5zkWKRH3UVQ2GCIxBZwrlgVfg3bIeLkHv3+JxZNhSInEwHNDT+v8jPT4FChIqp16AehSAwrRxf4NmiD9Ijbpdr2V5Eafh1qRT6qtOkFoUgMjxoN4F69PiLP7DOIUyvkiL96EtU7vw2xpRVs3bxQqdUbiDxV0A8x5w/BvVow3KrUhVBsgaod3oIqPxcp969CrciHxMa+YF+JJSTWtvBv2rlc9cOruH1iP0K6Fry3bBxd0Kj7QFw//Pz31tb5Be+t2m2M31sAkBRxDzeP/YMqjVqVVpVLXU1PW1hZiLDjRjI0OuBqfA6uxuegbRXjzyt/Zyt82r4Sjj9IR4rM8POqZaAzwlNzceh+GnQA7ibn4rNdd5EsU5ioJeZDIBCU+Y+54hT0cszLy8tgZb7yYNasWXjzzTcBAA8ePMCgQYNQq1YtNGvWrEzrlZOdjdSUFAQGVtKX+QcEIiUlGVlZmXBwcDSI79i5K7b9vRk9unWCSCSCpaUlflq0DGKLgsxeXl4uvvz8Y3w5YzZ27dgGpfL1P/Fu2X8Zi9cfg6+nU1lXxWQqudviYXIudLonZRHJuahWwc4oVigAvu1fBwv+uQcfZ2tU9rB9apsA+UqNQbxGq4O/m3kPML0cpEjIVuCp7kFCtgK+jsaZgBSZEh9suw2lRodO1dwMth19YDh1z81WAm8HKSIfDcjNlTxXBllmOly8K+rLXCr4QpaRhnxZNqwKmdraZ9JXsHN2xfHNK5GZnKAv12l1EFlIIBSJ9GVCoQjpCXGl24hSIs+VITczHS5eT75n1bmCL3Iz0yCXZUNaSN+8OXE6bJ1ccXrrKmQ91TcAkBz1AEKhCGd3rIVAIETVhq3QrNc7EP+L72EtTcp8GeTZ6bDz8NWX2bn7QJ6VDmVuDiQ2hueYnKQYuFWto39tYWUDSzsnZCVEQiAQQGrvBInVk3OOnYcPku9e0b9u8t5UWDm64PY/a5Gb9iRzbOPqiWZPTUcHgMSb5+DoWwnlRU5SDGzdfSAQPskJ2bn7IDsh0iAuNzUeOq3GoE9tPXyQnRBVcJzkWNh7PnkvCgQC2Lp5ITshCu7VgtF05HSD4yXeOAcHn8ol36BSpsiTITcrHc5PvbecXvLe6jah4L115u9VyEoxfG8p5fnY99s8tB82GbdPHYDmmZmA5sLbQYq4bLnB51V8lhx+hWSuk3IUGL/lFhRqLboGuRtsC3SxQnKOEuNaVkSdCnbIzFdh09VExGW9/teBZDrMgJdjsbGxCAoKwpYtWzBu3DiMHj0awcHB6N27N+7duweg4Ivgp02bhgYNGqB79+6IiIjQ769SqfDNN9+gZcuWCA0N1Werk5KSEBISgiNHjgAAdu3ahdatW0Mmk71S/SpVqoTg4GDcuXOnZBr8L+Tl5wEApNInJ9rHv8vlcqN4pUqJqtWqY+2GrTh2+iKGDh+Fjz+ciMzMDADAnFkz0LFzV9StF2yC2pcPianZ0D09Ev0PsJaIkK8yHDjnqzSQWoiMYsd3qIzItDzsuWY8LXLvjST0buiNOr4OEAsF6NPQG4FuNrAUm/cp1lIshFKtNShTarSQiI3vOuertFBqXv7342xtgcmh/jj+MEOfETdXSnlB/S0snzxL+/h3laLwizU7Z9dCy32q1YJAIMCJrauhVimRGheFy4d2Qq1SFhpf3qkUBX0jljyZWfT4d9VzbmjaOhXeN/JcGVy8K6Jqo1AMnbsCvafMQdTNizi+sfBHjMoDjaLgc+fp56wf/65RGbdfrZRDZGH4TLZIYgmNUgG1Qm70vLbIwtLgOFaOLi+tk06rxdXNSyBLiUf1TgOK3phSVnjbJVA/83eiVsghEIkhFD3JHYmf6oeCfjK8IfNsPz12/9BmxF87hVpvvldSzTCZx+edknhvAcDhVQtRtVFrVKhcswRraXrSQj6vFGotJIV8DuertFA8E/uYjaUYoZWdcTYqE6M33sC6SwkY26IiAl04BZ1KDjPgZuLAgQP45Zdf8OOPP2LWrFmYP38+li1bhtWrV+PmzZs4ePAg0tLSMHjwYLRqVTCFaNmyZbh69Sq2bdsGlUqF4cOHw9vbG2FhYZgyZQq+/vpr1KhRA7Nnz8a8efNga2v7kloYunPnDq5fv44JEyaURpNf6uuZ07FnV8FUz8cDR4XiyWBb/uhDytrKOAv57dzZaNSkKapWqw4AGPreSOz4ewsOH9wPicQScXGx/5lF1/7L8pTGg20rCxHylGqDsoYBTuhSxxO9fjpd6HF2X02Eq60E3/avAxtLEfZcS8Sp8DRk56sLjTcXCrUWFiLDixeJSPjcC5eXqeZmgzEt/HAmMhPrLie8fIdy6J8VP+DGyYMGZWrlk0Hy44G3RPpqF2tSaxv0m/I1DqxehPN7t8DNxx+1WnTAxf3b/n2lTeTAHz/izulDAJ6ck9VPDX7UyoLzs4XlK/aNjS36ffqt/rWTpw8avzEQR9cuQZuBo/9ttUvM5Q2/IObR88h4lIfTPHUDRfNocCSyNF77QCSxNIh9HC+2lAI6QPvsNpXCYAD2Mso8GS6smo/8rDS0HD8XUrvyM9NJLJEW0nZlQdufIpJYQqdRQ6vR6GeKqFUKiCRWj45TSB8+008alRJXNy1Cyv1raD52Nuw8Cn/cqLw5tPJH3C3Ce+tVzzu3Tx1AdkoCOrz3QQnVtOzI1VpInvm8shQLIVe92ueVWqPFrUQZzj9avO1yXDZuJOagga8DHqaZ903jkmbOU8DLGgfgZqJq1apo164dAKBz586YMaNgStn+/fsxZMgQODo6wtHRET179kRaWhoAYNu2bZgxYwacnQuefxk2bBi2bt2KsLAw9OvXDzt37kTfvn3Rvn17tGjRokj1mDZtGmbOnAm1Wo38/HyEhoYiMDCwFFr8cp99MR2ffTFd/7pz+1aIjHgIH9+CaVmREQ/h6uYGO3vj6VhJSYlQqw0HR2KxGBYWEuzZtQPh9+6iXWhTAIBCLodOB9y5fQvrN5nPxTC9XHiSDKPaBBiUBbjbIDwp16Cse3AFuNtb4uhnoQAAiVgIsVCA89PbouH0Q3Czk+Dw7RSsPBkNoGC6+qFPWmHLBfOcPvxYXJYc3Z6ZnlfB3rJYU/FaBTrh7fpeWHc5wWhKujnpPOx/6Dzsf/rXC8e9hbT4GDh5eAEA0hJiYOvoDKnNq93QVKuU0Go0GDT1O33Zkb+WwzOgSonU2xTavzsR7d+dqH+99H8DkB4fA0f3gr5JT4iBTTH6Jj0hBrdO7EeLvsP0ZRqVCiKLF6/4bGrB/cYiuN9Y/es9096BLCkWtq4VABRMkZbaOxtMJX/M3tMPOcmx+teq/FzIszNg51HweZaflQ6VPA8W0oIbyjlJsbDz9DU6TmFyUxNx8tcvYefph1YT5umPUV7YefpBdnATdDqd/oI+JznWYKo5ANi6eUMgFEGWEgd7z4J+kT3VD3aefpA91Yc6nQ6y5DjYPYpVyLJxZtkMCEQihP5vAaT25ecmxMu0HTIRbYc8eW8tnzQAGQlP3lsZCTGwcXCGpfWrvbfunj6E1JgILB3fF0DBeQg6HZKjwjHwq8K/D7m8is2U481ahp9XXg5SxGUZz4J8kbgsBao+szCbUCAAh5pUksx7fuR/yONBNACIRCJoH63qmZaWBje3J89benl56X9PSEjAmDFjEBISgpCQEMycORPJyckACu5a9ezZE0lJSQgLCytyPWbMmIELFy7gypUrOHz4MNLS0jB79ux/27wS0a17Dyz7dTFSU1OQmpqC35YuQbfuPQqNbRnaGutWr8TDB+FQq9XYsH4N0tJS0bR5C/y85DccO30RR06cw5ET59D9zV7o3DWMg+/X0LmH6ZCrtBjdNhAWIgFaVnVFq2qu2H453iDuyy23UP/Lg2g4/RAaTj+Enw88wMXITDScXpCRaFzJBb8PD4G7nSWkFkJM6lQFGq0OB2+llEWzSsyd5FwoNVp0r+kOkVCA2hVsUcfLHqcjM17pOHW97DA4xBs/Ho8y68F3YWq37IgTf6+GLDMdssx0nPx7DWq37PjKx9HpdFg/9yPcOlPwdWZRt67i8sGdCOnYsxRqbRpBzTvg7PY1yM1MR25mOs5uX4ug5h1e+ThSGztcPbQDF/dsglarQXpCDM5uX4ParbuWQq1Ljl/Dtrizbz3k2RmQZ2fg7r6/4New7XNi2yH63AFkRN+DRqnAjR2/w7liVdi5e8PO3RtOflVwY/vv0CgVyIi+h+jzB+HXsN1L66CS5+Hkki/gWrk2mgz7vNwNvgHAtVItiCwkuHdgA7RqFZJuX0TynYvwadDaIE5sKYVX3Wa4tfNPqPJzIUuJx4Nj2+EXUtCnPg1aI+n2RSTdvgitWoV7+/+CSCKFa6Va0Gk1OPvbV5DY2KP5mNlmNfguTPXmHXBu+xrkZqUjNysd53asRfVivLd6fPA1Ri/+G+//sgXv/7IFQS06oFqTNmY3+AaAW0kyKDU69KztAZFQgLpedqjrZYcTD1/tM+fYg3QEuFihdWVnCAAEe9sjyNMWZ6IyS6Xe5qysF2Az5ww8M+BmztXVFUlJT55JTUlJMdi2cOFC1KxZ8FxPVlaW/nno3NxcLFy4EGFhYfj666+xefNmiMWv9ufg5eWF7t2746+//iqBlvx7I0ePg1wux8C3ekGtUqFj5654f8x4/fZ+Pbuhc1h3DBs+CiNHjYVOB0wYOxIymQxVqlTFz0uWw8Xl+c9J0etHpdFh+IqLmN4jCMNDA5CSo8BH66/jQXIuGvg7YtmwBgj77iQSMl98B33nlQRU8bDFlolNYSkW4nJUJt5ZdsHoeTRzo9HqsOBIBN4J8UbXGm7Iyldj6eloxGcrUNXNGpNDA/DZ7ntIz3vxoj3dgtwhEggwsWVFg/Kt15Ow925qaTah1LXoNRgqhRwrPh8NjUaFoCat0bL3O/rtyz4ejprN2qLZm2+/8DgWEkv0+t90HFi9GLuXLYC9izs6vjseleo2LO0mlJomPQZBpVRg9bSx0KpVqNa4NZo+9dVhf342AjWatkWj7i9+Htna3hE9J8/Csb+W4cy2NbCQSlE7tCtCOvcp7Sb8K9U7vw2NUoFD306EVqOGT3BL1OgyUL/9wNwx8G3QGtU69INH9foI6joE51fOh0KWBZeAGmj0zpNvNGn87qe4svEX7Jn+DsSWVgjqMhge1eu/tA6xl44hNy0R8pwMxF46pi+3c/dGmw9+KNH2FpdQbIGmI6fj6qbFCD+0BZb2Tqj/9iTYe/oh7eFNnF46A20//gXWTm6o22c0rm1ZigNfj4JAIIB/sy7wb9YZQMEsggaDPsDN7SuQl5kKBy9/NBnxJYRiCyTfvYyM6HsQiiXYM/XJ/4HIwgJdZq4pq6YXW+M3B0GtVGDd9IL3VpVGrdGkx5P31uqpI1CtSVs07FZ+nvUvbRqtDnMPPsB7jX3RraY7suQqLDoZjbgsBaq52+CTdoH4cPsdpOW++PMqJlOOOQce4u36FTA4xBsZ+Sr8cjwK0RmvlkknehGB7r+26pIZiY2NRceOHTFr1ixs374df/zxBwDg7NmzmDp1Kvbv3481a9Zgw4YNWL58OfLy8jBo0CA0a9YMc+fOxYIFC3D//n3MmzcPAoEAEydOhJ+fH6ZPn46ZM2ciMTERCxcuRL9+/dChQweMGjXqhfUZPHgw+vTpo18FPSMjA+PGjYOfnx/mzHn589I5cvMejJQW96Zl8wy9ufDr2P3lQf9RTetVKOsqlGutKzuWdRXKLbmZ3xwqbZEZ5rn4nSloedn4XBUdLV8e9B924kFmWVehXFs3pF5ZV+GVOAxYVdZVQNa6wWVdhWJhBtzMDRgwALGxsejSpQscHR3RsmVLaDQFqzqPHTsW3377LcLCwqBQKBAaGoqPPvoIV65cwZYtW7Bz504IhUJMmzYNQ4YMQceOHREQEPDCf2/q1KmYPn06AMDCwgItW7bEZ599VtrNJCIiIiKi8sJ8Z4CXOWbAyWSYAS8cM+Avxgz48zED/mLMgD8fM+Avxgz48zED/nzMgL8YM+AvZnYZ8LfLQQZ8LTPgRERERERE9Joz50XQyhoH4KQ3efJkHD58uNBtY8aMwYgRI0xcIyIiIiIiotcHB+Ck99133708iIiIiIiIiIqFA3AiIiIiIiIqMk5BLz5hWVeAiIiIiIiI6L+AGXAiIiIiIiIqMmbAi48ZcCIiIiIiIiIT4ACciIiIiIiIyAQ4BZ2IiIiIiIiKjFPQi48ZcCIiIiIiIiITYAaciIiIiIiIio4J8GJjBpyIiIiIiIjIBDgAJyIiIiIiIjIBTkEnIiIiIiKiIuMibMXHDDgRERERERGRCTADTkREREREREXGDHjxMQNOREREREREZAIcgBMRERERERGZAKegExERERERUZFxCnrxMQNOREREREREZAIcgBMRERERERGZAKegExERERERUdFxBnqxMQNOREREREREZALMgBMREREREVGRcRG24mMGnIiIiIiIiMgEmAEnk7kZm13WVSiX/Dp2L+sqlGvR+3aUdRXKrab1RpZ1Fco1Gwt+xD2PjUVZ16B823YlqayrUG7FxPGz/HlWjWhS1lUo15yteeIhAjgAJyIiIiIiolfAKejFxynoRERERERERCbADDgREREREREVGTPgxccMOBEREREREZEJcABOREREREREZAKcgk5ERERERERFxinoxccMOBEREREREZEJMANORERERERERccEeLExA05ERERERERkAhyAExEREREREZkAp6ATERERERFRkXERtuJjBpyIiIiIiIjIBJgBJyIiIiIioiJjBrz4mAEnIiIiIiIiMgEOwImIiIiIiIhMgFPQiYiIiIiIqMg4Bb34mAEnIiIiIiIiMgFmwImIiIiIiKjomAAvNmbAiYiIiIiIiEyAA3AiIiIiIiIiE+AUdCIiIiIiIioyLsJWfMyAExEREREREZkAB+BEREREREREJsAp6ERERERERFRknIJefMyAExEREREREZkAM+BERERERERUZMyAFx8z4EREREREREQmwAw4vTbUajXWLvseZ4/th1ajReNW7TFo1AcQW1i8cL97N69gzidjMO+3TXDz8AIA3LxyHht+/xmJcdGwtrFFyw7d0ePt4RAKzfOeVWUPG3zVsyaqe9khNUeJBf/cw97rSS/c56teQfB3tcGQpef1ZQOa+OLdlhXhamuJm3HZ+HrHHdxJyCnt6pcrgb6uOL5qCur3noWktNe/7V72lni3kQ/8HKXIkqux8WoiLsRkPTdeIhLg47aB2Hc3FWejn8S1qeyMTtXd4CAVIy1XhW03knD+BccxFxq1GrtX/oLrpw5Bq9WidtM2CBs6HmKx8XlHp9Ph0KY/cenIP5Dn5sDVyw8d3x6BSrXqAwCUCjn2rvkVty+chEouR9X6TfDm8EmQSK1M3awSUZJ9c/30YWz8aRbEFhL9Ps279UO7fkNN1p6S5OdkhQmhAQh0tUZGngq/n4nBiYfpRnHudhK839wfNSvYQqsDLsVk4deTUciWqwEAX3Wthrre9tDodPp9xm+8gbgsucnaUhoqudtg2ps1UN3TDqkyJb7fdx/7bya/cJ9pb9aAv6s1hi6/qC9bNDgYjQOdoNE+6Z++i84iKi2v1Opuamq1GiuXLMDpI/ug1WrRNLQD3h0z5bnXPh8M74uUpHgIBE+uZ37bdBAWEkmh8eZGo1bjn5W/4Mbpw9BptajZtA26vDvuueedI5v+xJWj/yA/VwZXL1+0HzACgY/OOw+vX8L+dUuRFh8DqbUtgtt0QWjvIWZ7LUjlBwfg9NrYumYpIsPvYM6Sv6DVaPDDzCnYtXkV3uw/7Ln75OZk49cF06HVavRlsuws/PDVB3hv4lQ0btUBqckJmPXhCFTwqYimrTuZoiklykIkwK/v1sf6s7EYsvQ8mlZ2wY+D6uJGbBbiMgq/SOtYywN9GvrgQkSGvqxTbQ98FFYVo/+4jPMRGejXyAd/jmyIrgtOIE2mNFVzylSfjvUxf0ofODvYlHVVTEIkFGBSaACOhKfhm4MPEORpi7HNKyIyPQ+puSqj+IpOVhjayBv+ztbA3VR9eV0vO/Su44kFRyIQkZ6POhXsMK5FRSTJFIh+zt+guTi48XfEP7yHid/9Ca1Gg9Xzp+L4tvVo03uwUezZfX/jyvH9GPr5fDh7euHy0b1YM+9zTPpxNeycXLBl0Vwo5XKMn7ccOuiwZv5UHPhrBbq+M7YMWvbvlWTfxEfcR3DrLug56sMyaEnJEgsFmNG1KnbfSsbH22+jnrc9Pu9UBfdSZEjOMTyXft6xCh6k5mHIqiuwEArwYbtKmBAagFl77wMAKrla49Mdt3ErUVYWTSkVYpEAvwwOxsbzsRi2/CIaV3LG9/3r4GZcNuIzCz9ftA9yR68G3rgUlWFQXsPLDsN/v4jL0eZ/s+95Nq78FRH372DBb5ug0Wjw7fTJ2L7hT/QaONwoVp6fj/jYKPyyeiecXd3LoLal7/DGP5AQcR/jFvwBrUaDdd9Oxcnt6xHay/i8c37fNlw7vh+DPys471w5ug/r5k/FhB9WQSQWY+38z/Hm+1NQq2kbZKYkYfm08XCp4Is6LdqVQcvKH05BLz7ewimn4uPjERIS8kr7LFq0CJ988kkp1QiIiYnBBx98gKZNm6Jhw4YYNGgQLl68+PIdTeTEgV3o1vcd2Ds4wdHZFT0GvIdDu7e8cJ8VP81G09adDcps7R3w0+o9aBLaERq1GplpqdBoNLBzcCzF2peexpWcYWMpxm9HI6DW6nD8XiqO30tF30Y+hcZ7OUrxUdeqWH8mxqC8c20PbL0QjzMP0qHR6rDuTAzSZAp0ru1pimaUuTEDQvHF6DDMWLSjrKtiMjXcbSC1EGL37RRodMD1BBmuJ+QgtJKzUayfkxQftgnAyYgMpOYaDiIcrSyw+3YKItLzAQDXEnIQny1HJRdrk7SjNF0+uheterwNG3tH2Dm5oE3vITi3f3uhsfkyGdr0GgxXL18IhSI0aNMVIgsJEiLDkZOZjlvnjqPHqA9gZWsHa1t79J80HU269DJxi0pOSfUNAMQ/vAfvwKqmrH6pqeNtDysLETZdToBGq8PFmCxcjM5ClxqGAyKpWIgsuRqrzsdCodZCptRgz61kBHnaAQBcbCzgYGWBB6mvTzYXABoFOMNGIsKK45FQa3U4eT8NJ+6noU+Id6HxFRyl+KBzFWw4F2tQ7m5nCSdrC9x+zWdpHdu3A2/2fxf2jk5wcnFFr4EjsH/n5kJjox7ehYOj02s7+AaAK8f2osWbA/TnndBeQ3Dheeed3By0euq8U79NF4gtJEiMDIe1nQOm/LoZtZu1hUajRk5GKrQaDWzsHUzcInodMQNeTnl5eeHChQtlXQ29uLg49OnTB++88w5mzJgBKysr7Nq1CyNHjsTq1atRo0aNMq1friwHmemp8PYL0JdV8KmIzLQUyHKyYGtnfMI8uGszVEol2nfrix1//W6wzcq6IMM5dkBHyPPz0LR1JwTVbVi6jSglldxt8TA5F0/NUEREci6qVbAzihUKgG/718GCf+7Bx9kalT1sn9omQL5SYxCv0erg72b+g6ii2LL/MhavPwZfT6eyrorJeDlIkZCtwFN/OkjIVsDX0XhKdIpMiQ+23YZSo0Onam4G244+MJxa62YrgbeDFJGPBuTmKj9XhpyMNLj7+OvL3Lz9kJORijxZNqxt7Q3in838Rt65DkV+LjwrVkJ8xD3YOjrj1rkTOLl7E9RKBWo2aoVOg0aZoiklriT7BgASIu9DKBLhyNbVEAiEqN20Ndr1G2aW02b9nKwQmyk3eF/FZOYj4JkbUnK1Fl/uumtQ1tjfCeEpuQCAyq42yFNq8EWnKqjsZoMUmRKrz8fibFRmKbegdAW62SAi9ZnPrNRcVPUo/DPrm7618MO+cPg4W6Gyx5PZSTW87JCr0ODHt+uihpc9ErPk+OXgAxx9anaOucuV5SAjPRU+foH6Mm9ff2SkpUCWnQXbZwaLEffvwMLCEl/+bxgSYqPg5euPt4ePR7Wa9Uxc89KRnyuDLCMNbk+dd1y9/ZCTkVboeefZrHjUo/OOx6PzjqVVwXty3oieUObnoXbzdgh4ND2dADABXmzMgJdTsbGxCAoKwpYtWzBu3DiMHj0awcHB6N27N+7duwcAUKlUmDZtGho0aIDu3bsjIiJCv79KpcI333yDli1bIjQ0FEuXLgUAJCUlISQkBEeOHAEA7Nq1C61bt4ZM9uLpawsXLkSLFi0wZswY2NraQiQS4Y033sCoUaPw8OHD0umEV6CQF2QAJJZSfdnj35UK4ylrsVEPsXvzKgyf9MULj7tw7T+Ys3g9wm9fx7Z1y0uwxqZjLREhX2U4cM5XaSC1EBnFju9QGZFpedhzzfj58L03ktC7oTfq+DpALBSgT0NvBLrZwFL83ziNJKZmQ/f0FeF/gKVYCKVaa1Cm1GghERt/6uartFBqXt4/ztYWmBzqj+MPM/QZcXOllBfU30JiqS97/LtKoXjhvolRD7D+++lo/9Z7sHd2Rb4sB7LMdMSG38bYuUvx/uzFiLl/C/vWLiu9BpSiEu2bXBncffxRu2kbTPphFYZ+Ph/hV89j39pfS68BpUgqFkLxzPtKoda+9Fzap14FtAh0xtJTUQAAC5EQd5Jl+O10NAauvIwNl+PxWccqqOJm3o/IWFuKIFcZ9o9cpYFUYtw/Y9pWQlRaHvbeMP7MshQLcS02Cwv+uY+23xzD8mOR+K5/HdT0sjeKNVfy/EfXPtKnrn0e/a4o5NoHAAKr1sDYj7/CorV70LhlO8z9fCJSkxNLv7Im8G/POxt/mIG2/YbB3tnVYNuUX7dg7ILfEXP/Fo5uXlnCtab/ImbAzcCBAwfwyy+/4Mcff8SsWbMwf/58LFu2DKtXr8bNmzdx8OBBpKWlYfDgwWjVqhUAYNmyZbh69Sq2bdsGlUqF4cOHw9vbG2FhYZgyZQq+/vpr1KhRA7Nnz8a8efNga2v7wjqcOHECn376qVH5yJEjS6XNRfHHz3Nx6vA/AKAfGCmVT06wjwfellLDrIJSIcfieV9gyJiPYO/ojMz0tOf+GxKJJbz8AtCpxwAc2bsNPQeOKOlmlLo8pfFg28pChDyl2qCsYYATutTxRK+fThd6nN1XE+FqK8G3/evAxlKEPdcScSo8Ddn56kLjyfwp1FpYiAwveiUi48FDUVVzs8GYFn44E5mJdZcTSqKKJrdt2Xe4enw/AOgzmKqnzjuPf3/Rwmk3Th/B1l+/RZveg9Gi+1sAALHYAlqtFp0HvQ+ptQ2k1jZo+eYA7F75C8LeHVc6jSlhpdU3Vja2GD79R32Mq5cvWvcegt1//oywd8eXcCtKn1xtfBPLUiw0ulH6mIVIgPGtAlDPxx4fbbuF2EfPQZ94mG6wcNvxB+loV9UVTQOccP9Rltwc5Ss1RjcjpBYi5CkM+yfE3wmdanmg3+KzhR5n381k7Htq4ba9N5LwRr0KaBvkhpvx2SVfcRP57cevcfzgnoIXj699nhpcKuUFfx9SK+PZaZ179EfnHv31r7v2ehuH9vyNaxfPoG2XHqVX6VK047fvce3Reecx9Sued26eOYLtvy5Aq16D0PzReedpFhIJ3LwromnX3rh4aDfa9H23ZCpP/1kcgJuBqlWrol27ggUfOnfujBkzZgAA9u/fjyFDhsDR0RGOjo7o2bMn0tIKBpPbtm3DjBkz4Oxc8KzmsGHDsHXrVoSFhaFfv37YuXMn+vbti/bt26NFixYvrUNmZiacnMrX1Nt3x32Cd8c9eeZ94uAwJMREwqNCwbPNCbFRcHR2hY2t4bS1iPu3kRQfgyXzCrLfjwfvU8cOxLvjPoF7BR/89sNMzP55DYSigoGrSqWEtc2Lb1KUV+FJMoxqE2BQFuBug/Akwwu07sEV4G5viaOfhQIAJGIhxEIBzk9vi4bTD8HNToLDt1Ow8mQ0gIKpf4c+aYUtF+JM0xAyubgsOboFGT4rWMHeEnFZL84kFKZVoBPeru+FdZcTjKakm5M3R0zGmyMm619/834fpMZHw8Wz4PnUlLho2Dm5wOo554tDm/7EqV0b0Xf8Z6jeoJm+3M27IgBAnpcLW8eC87ZOqwHMaNZFafVNSnw0Lh/Zi45vP7kBqlYpDVZENyfR6fl4q76XQZmvoxWiM4xnhNhLxfiqazVodDpM3HQTGflPFj9sXcUFao3OYBBuITKetWJuHiTLMLzVM59ZrjZ4+MxNhbC6nvCwt8TBKS0BPPnMOvV5azSbfQRd63hCrdEaDMIlhcw+MDfDJ36G4RM/078e3b8z4mMi4elVcO0TFxMJp0KufQDgn21/wdsvALWDG+nL1CoVLMz0vQQA3YdPQvfhk/Svvx3dF6nxMXB+dN5JjYuG7QvOO0c2r8TpXRvRa9ynqPbUeSc2/Da2LZmH0fN+g1BYcC2oVqkgtTbvGSYliYuwFd9/Y+6omXs8iAYAkUgErbbgwyMtLQ1ubk+etfTyevKBnpCQgDFjxiAkJAQhISGYOXMmkpMLPoQEAgF69uyJpKQkhIWFFakOrq6uSElJMSrPycmBUlk+VsBu3q4rtq1bgcz0NGSmp2HbuhVo3q6rUVy1WsH4besxLN5wEIs3HMTcXzcCAGb9sgZNW3eCT8VKkOfn4e91y6HRqBH14B72bluP1p3fNHWTSsS5h+mQq7QY3TYQFiIBWlZ1Ratqrth+Od4g7sstt1D/y4NoOP0QGk4/hJ8PPMDFyEw0nH4IANC4kgt+Hx4CdztLSC2EmNSpCjRaHQ7eMv67oNfDneRcKDVadK/pDpFQgNoVbFHHyx6nIzNevvNT6nrZYXCIN348HmXWg+/CBLfqhMObVyInMx05mek4smUlglsV/m0JZ/ZuxandmzB8xk8GA0wA8PALgE/lGtizahEU+XnITk/FsW3rUae5+a62W1J9Y21rj7P7tuHEjg3QajVIiY/GkS2r0LBdN1M0o8Rdi8+GQq1F//peEAsFaODrgBA/Bxy+ZzgbSygAZnSthmy5Gp9su20w+AYAK7EQo1tWhL+zFYQCoG1VV9TwtMWh++b9jPO5iAwo1BqMbB0AsUiA5lVc0KKqK3ZcMZw1M2PbbTSaeRjNZh9Bs9lHsPjwQ1yOzkSz2UcAFDx+9Vm36qjqYQuhAOherwLq+jpg5xXznH3zPK06dMPm1cuQmZ6KzPRUbFnzG1p1KPy9kZGWij9+mY/kxHioVSr8ve53yOV5aNC0lYlrXXrqteqIo0+dd45tWYV6rToWGntu7984s3sThk3/0WDwDQDuvgFQ5Ofj6OZV0Gg0SIgMx5ndm9CgbdGum4lehBlwM+bq6oqkpCfPPT09QHZ1dcXChQtRs2ZNAEBWVhbkj6Yl5ebmYuHChQgLC8PXX3+NzZs3Qyx+8Z9Cs2bNcPjwYbz5puEgdMGCBcjOzsZ3331XUs0qtp4DR0CpkOPLCYOhVqvQpFUH9HpqAaNPR/dHs9ad0f2td194HEupFB9M/x6rf12Asf07wt7RGT0GDEPztsaDeXOg0ugwfMVFTO8RhOGhAUjJUeCj9dfxIDkXDfwdsWxYA4R9dxIJz/l6l8d2XklAFQ9bbJnYFJZiIS5HZeKdZRfMPttCz6fR6rDgSATeCfFG1xpuyMpXY+npaMRnK1DVzRqTQwPw2e57SM8z/kqyp3ULcodIIMDElhUNyrdeT8JeM18QqW3fd6BSyvHLxyOgUatRp1kbg++m/umDd1GnRXu07jkIR7eugVKej6VTDb9WrPfYT1CzcSgGfzwHu1f+gu8nDoJGo0Gd5m3R/q3nf41ieVeSffPOp3OxZ/USHN78JyRSK4S061boVFFzoNbq8MXOOxjXKgB9g72QnqfE/IMPEJ2Rj5oV7DAzrBpGrb8GX0crVPewhUKtxV/DGuj3V6q16P/HJey5nQI7qQWmd60GB6kY0Rn5mLb7rtFXmZkbtUaH9/+8jC/eqI5hLfyRKlPgs0038DAlF/UrOmLJkGC88dNpJL7ku843XYiDo7UFfh5cD47WEkSk5GLs6ivP/Sozc9Vn8EgoFHJ8MmYg1Co1mrXuiL7vvK/f/uGIfmjetjN6DhiGvkNGQa1SYtr/hiEvLxeBVWvg069/NtsZfoVp3ecdqBQK/PrJSGjUatRq1gZt+j457/zy4VDUbt4erXoOxLG/C847v31h+JhPzzGfIKhxKwz8ZA72/LEQ84b3gI2DE0J7D0Hd5wzm/4uYAS8+ge6/tqqQmYiNjUXHjh0xa9YsbN++HX/88QcA4OzZs5g6dSr279+PNWvWYMOGDVi+fDny8vIwaNAgNGvWDHPnzsWCBQtw//59zJs3DwKBABMnToSfnx+mT5+OmTNnIjExEQsXLkS/fv3QoUMHjBr14pV2Y2Ji0KNHD4wYMQKDBw+GhYUF/v77b8yZMwerV6/WD/Rf5Ex4Zgn0zOvnnWWFP79GBaL3/Xe+9utVvfVR2a3BYA7Cari+PIioECtOx7w86D8qJs58n58ubatGNCnrKpRrd9L5t/MiA4IL/6q98qrSB3vKugp4sKBLWVehWDgF3YwNGDAAzZo1Q5cuXfDee++hZcuW+m1jx46Fj48PwsLC0K5dO7i4uOCjjz7ClStXsGXLFnz22WcQCoWYNm0alixZYrCCemF8fX2xevVqXLp0CW3btkWzZs2wY8cOLF++vEiDbyIiIiIiIlO7ePEiunfvjnr16mHo0KH6x3KfFhsbi+rVqyM4OFj/c+DAAQAFs4yHDRuG4OBgdO3aFRcvXvxX9WEGnEyGGfDCMQP+YsyAPx8z4C/GDDgVFzPgz8cM+PMxA/5izIC/mLllwCt/WPYZ8PBvX54Bl8vlaNeuHb744gu0bdsWc+bMQWZmJr7//nuDuIMHD2LlypX4888/jY7x/vvvw8/PDx9++CH++ecffPvttzh06NBLH+F9HmbAiYiIiIiI6LVz+vRpuLq6onPnzpBIJJg0aRIOHDgAmUxmEHf37l1Ur17daH+ZTIZjx45h7NixkEgkeOONN+Di4oKTJ08Wu05chI0AAJMnT8bhw4cL3TZmzBiMGGF+339NREREREQlz1wWYYuKikJgYKD+tb29Pezt7REVFWXwGO3du3eRkpKCtm3bAgD69++PkSNHIjo6Gk5OTnBwcNDH+vv74+HDhwgNDS1WnTgAJwAoF6uYExERERERFcXChQvx888/G5SNGzcO48eP17/Oy8uDVCo1iJFKpcjPzzcos7e3R/Xq1TFkyBAkJCRg1KhR8PDwgLe3d5H2fxUcgBMREREREZFZGT9+vMFguzBWVlZQKg2/nlEul8PGxsagbObMmfrfK1eujIEDB+LQoUMYOXIkFAqF0f7W1tbFrjefASciIiIiIqIiEwjK/qcoAgICEBkZqX+dk5ODrKws+Pn56cu0Wi0WLFiAtLQ0fZlKpYJEIkHFihWRmZlp8Mx4ZGSkwbT2V8UBOBEREREREb12mjRpgoSEBOzevRtKpRI//PADWrVqZZABFwqFuHz5Mn7++WcolUrcv38fa9asQffu3WFra4vmzZvjxx9/hFKpxK5du5CSkoLGjRsXu04cgBMREREREVGRCQSCMv8pCqlUisWLF2Pp0qVo3LgxIiMj8dVXXwEAgoODceHCBQDAvHnzEBsbi6ZNm+K9997DyJEj0apVKwDA7NmzERUVhaZNm2LJkiVYuHAhLC0ti913fAaciIiIiIiIXkt169bF33//bVR++fJl/e9eXl5YtmxZofu7urpi6dKlJVYfZsCJiIiIiIiITIAZcCIiIiIiIioyM/ka8HKJGXAiIiIiIiIiE+AAnIiIiIiIiMgEOAWdiIiIiIiIikwo5Bz04mIGnIiIiIiIiMgEmAEnIiIiIiKiIuMibMXHDDgRERERERGRCXAATkRERERERGQCnIJORERERERERSbgHPRiYwaciIiIiIiIyASYASciIiIiIqIiYwK8+JgBJyIiIiIiIjIBDsCJiIiIiIiITIBT0ImIiIiIiKjIuAhb8TEDTkRERERERGQCzIATERERERFRkTEDXnwcgJPJ7ApPKesqlEtN61Uo6yqUa03rjSzrKpRbf81bWtZVKNecZ4wr6yqUW3K1tqyrUK5ZSXh59DwNa3mWdRXKrbXX48u6CuVaFVdpWVeBqFzgFHQiIiIiIiIiE+AtXiIiIiIiIioyzkAvPmbAiYiIiIiIiEyAGXAiIiIiIiIqMi7CVnzMgBMRERERERGZAAfgRERERERERCbAKehERERERERUZJyBXnzMgBMRERERERGZAAfgRERERERERCbAKehERERERERUZFwFvfiYASciIiIiIiIyAWbAiYiIiIiIqMiYAC8+ZsCJiIiIiIiITIADcCIiIiIiIiIT4BR0IiIiIiIiKjIuwlZ8zIATERERERERmQAz4ERERERERFRkTIAXHzPgRERERERERCbAATgRERERERGRCXAKOhERERERERUZF2ErPmbAiYiIiIiIiEyAGXAiIiIiIiIqMibAi48ZcCIiIiIiIiIT4ACciIiIiIiIyAQ4BZ2IiIiIiIiKjIuwFR8z4EREREREREQmwAw4ERERERERFRkT4MXHATiZNa1GjStblyH60nHotBr4BrdEcO9REIktCo0PP7Ebdw5shCI3G66BQWjYfwKsndwAAJlxD3Fl62/IiH0AkYUlfOo2RZ03hkEsscSxJdOQ+uCmwbHUSjlqdxuCGh36lXo7/y0ve0u828gHfo5SZMnV2Hg1ERdisp4bLxEJ8HHbQOy7m4qz0U/i2lR2RqfqbnCQipGWq8K2G0k4/4LjmAP2TckJ9HXF8VVTUL/3LCSl5ZR1dUqFh50Efet4wttBihyFGjtvpeBagnFbLcVC9KvriWpuNtDodDgdmYl/7qbqtzfzd0RooDPspCJk5Kmx714qrsYXHMfWUoQ+tT1RydUaWp0Od5JzseV6EhRqrcnaWRwV7CwxILgCfBylyJarse1GEi7HG/eNVCzE28EVEORhC41Wh+ORGdh5K0W/3dvBEn3reMLHQYo8lQZ77qTidFQmAEAoALpUd0NjPwdYW4gQnpaHtZfika3QmKqZpcLXUYr3m1eEv4sVMvNVWH0+DqcjM43i3GwleK+JL2p42EKr0+FKXDZWnIlBjpm3/1le9pZ4p6G3/ry86WoiLsRmPzdeIhLgozaB2HcvFeeiDc+7DXzs0au2Bz7fc7+0q10ishOjcW3jL8iKj4ClnSOCwt6BV93mRnEqeR6ubvwFKXcvQSASw79JJ1TrPFA/LTjh+mnc2rUS8swU2HsFoG7fsbCv4I+8jBQcnjfW4Fg6jRpajRodv/wdlnZOuLt3LaLPHYBKkQ87D18EhQ2Ba+U6Jmn/v6VRq3Fk7RLcOXsEOq0W1RqHou2gMc+9Lnzswp5NiLp5Cb0//FpfplLIceyv3xB++RTUCgUC6jZCh3cnwMLSqrSbQa85DsDJrN3YvQYZMeHo/Nli6DQanPxtJu4e3IygTv2NYhPvXMKN3asROmYW7Ny9cWXrMpxb+wNaj50NjVqF47/OQGCzzmg1+ivIczJxavls3Ni1EvV6jkCr92cYHOvOwU2IOn8ElVt2M1VTi00kFGBSaACOhKfhm4MPEORpi7HNKyIyPQ+puSqj+IpOVhjayBv+ztbAUwOGul526F3HEwuORCAiPR91KthhXIuKSJIpEJ0hN2WTSgz7puT06Vgf86f0gbODTVlXpdSIBMDwRj44HZWJRaeiUdXVBu809EZMphwZ+YZ/L33reEAsFOCr/eGwl4oxsokvUnKVuBibjRruNuha3Q2/nolBTKYcNdxt8G5Db6TKlIjLVqBXbQ/kqTSYvi8cEpEAQxt6I6yGG7ZcTyqjlr+cSACMbuaLExEZ+OF4JKq722B4Y19EHXiA9DzDvhkQXAEWIgE+33MP9lILjGvuh+QcJc7FZMFeKsaklv7YeiMJPx6PQqCLFca3qIiYzHzEZinwZk13VHWzwYKjkchTafBOA2/0D66ApWdiy6jl/55YKMBnHStj350UfLn7Lup42ePDdoEIT72FFJnSIHZK20A8TMvDiPXXYCESYmKoP0a3qIh5Bx+WUe1LnkgowP9a+ePIg3TMO/QQNTxsMba5HyIy7iOt0POyFO829IG/sxVw70m5WChA1xpu6B7khpRcpdF+5ZFWrcK55V+hYpPOaDZmNlLvX8X5P7+Bo29lWDt7GMRe27QIWpUSHb5YAXl2Bs4smw4bd2/4NmiDnKQYXFr7HRoNmwqXgCDcP7wZ5/+Yi7YfL4K1kxvC5mzQH0ejUuLEzx/DM6gRpA4uiDi5G3FXT6LFhPmwcnRF1Jm9OLdiNjp9tfqlg9jy4NTWlUiKvI+hc36DVqvBth+m4/yuDWjy5sBC4xV5uTi55Q9c3r8N/rVDDLb9s+xbqBT5eGfWr9DpdNj243Sc2PQH2gwcbYqm0GuMz4CXU/Hx8QgJCXl54FMWLVqETz75pFTq88knn6BWrVoIDg5GvXr10LhxY3zwwQdISUl5+c6lKPLcQVRv3w9SWwdYOTgjqNMAPDi5u9DYiLMH4N+wDZx8AiGWWKLOG0OREn4dOclxyM9Mg4OXP2p07AehSAxrR1dUbNgWqRG3jY6TEfsAt/5ZjybvfgQLqXVpN/Ffq+FuA6mFELtvp0CjA64nyHA9IQehlZyNYv2cpPiwTQBORmQg9ZkLFkcrC+y+nYKI9HwAwLWEHMRny1HJpfz3wfOwb0rGmAGh+GJ0GGYs2lHWVSlVlV1tYCkW4nB4OrQ64E5KLu4ky9CkooNBnEQkQF0ve/xzNxVKjQ6puSoce5COZv6OAAAHqRiHwtMQk1lwc+Z2ci4Sc5So6FyQVfGwlUAoePIBrdMBKk35zn5XdbOBVCzE/ntp0OqAW0m5uJUkQ4tHbX5MIhKgvrc9dt5KgUKjQ0quEofC09Ay0AkA0NjXAZEZ+TgZmQkdgAdp+Zh76CFSc1UQCoAWAU7YcDURWXI1VBod1lyOx7YbyaZvcAmqVcEOVhYi/H0tCRodcDkuG1dis9GhmqtBnFQsRLZcjb8uxUOp0SFXqcG+O6mo7mFbRjUvHY/Py3senZdvJMpwPVGG0MBCzsuOUnzQOgCnIo3Py+839UVVN2vsvpNqtF95lRp+HWp5Piq36QWhSAz36g3gXr0+os7sM4hTK+SIv3oS1Tq/DbGlFWzdvFCp1RuIPLUHABBz4RDcqgXDrUpdCMUWqNr+LajkuUi5f9Xo37y9ZxXEEimqdixIXPg364LQSd/D2skNqrwcqPJyYGFtB4HAPIYMN0/sQ6Nu/WFt7whbRxc06TEQVw/tfG78xm8+Qm5WJuq2NUyo5Gam4/7FE+gwdBKkNnawsrVH97FTUb9jj1JugfkQCARl/mOumAEvp7y8vHDhwoWyroaBMWPGYMyYMQCAjIwMfPXVVxgxYgQ2b94MkUhk8voo82SQZ6fD3tNXX2bn4YP8rHQocnNgaWNnEJ+TFAOPqnX1ryVWNpDaOSErIQo+dZsZZbnjb5yDk09lo3/30sbFqNqmJxw8/Uq4RaXDy0GKhGwFdE+VJWQr4OtoPIUqRabEB9tuQ6nRoVM1N4NtRx+kG7x2s5XA20GKyEeDTnPEvikZW/ZfxuL1x+Dr6VTWVSlVHnYSJMuUBn8vyTIlKthbGsS52kggEgqQlKPQlyXJlKhgVxB35pkpsi7WFvC0kyD20YD84P109K3riRBfBwgFAkSk52FPOR9EeNpZIjHHsG+SchTwdpAaxLnbFvRNwlN9k5ijhNejPvRzkiI1V4mhDb1Rw90G2XI1dt5OQWKOEp52ElhZiOBibYFB9b1gIxHhdrIMG68mmqKJpcbHUYq4TLlB38VmyeHvZHgekqu1mLUv3KCsYUUHPEjNM0EtTaeCvaXReTkxWwEfR6lRbEquEh9uvwOlRoeOz9ywWH0pHpn5ajQPcCzdCpegnOQY2Lr7QCB8Mti1dfdBdnykQVxuajx0Wg3sPHwN4nISogAAsqRY2FWoqN8mEAhg6+qFnMQouFcLfvLvJUYj8uRutP7wJ/1gRiAQQGwpRfzVk7iwah6EQhFChnwMYRlc570qea4MuZnpcPF+cn3mXMEXssw05MuyYWVrb7TPmxNnwM7ZFae2rkRWypNzSVJUOGwcnBF+8SQu7t0CtUqBKiEt0eqt4SZpC73ezON21n9QbGwsgoKCsGXLFowbNw6jR49GcHAwevfujXv3CuZYqVQqTJs2DQ0aNED37t0RERGh31+lUuGbb75By5YtERoaiqVLlwIAkpKSEBISgiNHjgAAdu3ahdatW0Mmk71S/ZycnDBnzhzExcXh8OHDJdPoV6RWFlysiiVPLn4f/65RKYzjFXKILAwvlEUSS2iUhrE6rRaXNi1GTkocanYeYLAt8c5l5CTHoVrbniXSBlOwFAuhfObZUaVGC4nY+M5hvkoLpUZnVP4sZ2sLTA71x/GHGfqsrzli35SMxNRs6HQv7xtzJxEJoXrmb0Cp0UEiMvwotRQLodbqoH0qVKXRwUJk/JHraCXG8MY+OBeThehHA3CBADgVmYHP99zHjH3h0OmAXrU9jPYtTyzFQqMsfUHfCIzijPtGq+9DG4kITSs64nJcNj7ZfQ9/30zGu4+eBbaRiKDV6dDYzxHfH4/EV/vDYSMRYXADr1JvX2mSWgiheLbv1FpIxC++ROtR2wNN/J3wx9mY0qyeyUnFQiif6Q/FU38jT3vReTkzX10q9StNGoUcIskz1ykWEqNrGrVCDoFIDKFI/FScpT5OrZRDZCExPE4h1zv3DmyET/3WsHXzNqqLR81G6PbNZtTu/T4urJqHnKTy/5iHSlHwmWsheXKz5vHvaqXxdSEA2Dm7Floul2UjNysdiQ/vYvDMRRg4bSESHtzGiY0rSrjW5ksgKPsfc8UBuBk4cOAA+vTpg7Nnz6JmzZqYP38+AGD16tW4efMmDh48iB9++AEnT57U77Ns2TJcvXoV27Ztw4YNG7Bjxw7s2rULHh4emDJlCr7++mskJSVh9uzZmDVrFmxtX30Km1QqRXBwMC5dulRibX2ZC3/9jC1T+mDLlD7YM3sUgILnlx57fIIVS4zvlIsklgaxAKBRKiC2fBKrzJPh+NIZSAm/gbYTvoHU3jCjF3FmHyo2bGsWU88fU6i1Rhf+EpGw2As6VXOzwbROlXEtPgcrL8SVRBXLDPuGXoVSo4XFMwNKiUhg9PeiVGshFgogfCrUQiQwGlRUcrHCpFb+uJOci83XCp7vtrMU4a16FXDgfhoUai2y5AULvTX0dYBYWH6vNgr65tn3knHfKNS6QvrmyXtOrdXhXkoersTnQPto+vHd5FzU8bKDWquDUCDArtspkCk0yFNpsfNWCmp52pXrvnkZuUprdKNCIhZC/pzzkIVIgHEtK6JrkDu+3HUPcVmFDyzMlUJtPNi2FAmhUL9eC80VRiSRGl+nqJRG1zRiieWjhdM0T8UpIJJY6bdrC7neET11vaPKz0XCtZMIeM5aNiKxBYQiMSo27gg7Tz8k3ylfszIf2//Hj/hp5Bv4aeQbWPHxMACGg23Vo2TNqy6cJrKQQKfVolX/EbC0soGdsxsahfXDvQvHS67y9J/FAbgZqFq1Ktq1aweJRILOnTsjOjoaALB//34MGTIEjo6OqFSpEnr2fJKV3bZtGyZMmABnZ2d4eHhg2LBh2Lp1KwCgX79+8PDwQN++fdG+fXu0aNGi2HWzt7d/5ez5vxHy1jj0mr8JveZvQu/5myG1d0b2U3dlc5JiIbV3hsTa+IaCg2dF5CQ/iVXm50KenQH7R1PJZamJOLBgEoQiMdr+b75+dfTHtBo1Em6eh1+DVqXUutIRlyU3miJbwd6yWBdtrQKdMCnUH1uuJWHd5YSSqmKZYd/Qq0jMUcDN1jCr5G4rMZhqDhRMi9VodQaxHrYSJD4V19jPAcMb+2LP7RRsu/nkGWZ7SzHEQgFET93a1+h00AHQluNZBgnZCng80zcedpZIyDEcBCTLFNBodfCwffK+87R70jeJ2UpYWRhemggFAggAJOUU9OvT24XmnAJ5JCYz32iqvo+DFLEZxjNo7CxFmBVWDV4OUny0/TaiCokxd3HZCnjaGZ6XPe0tEZ/9et1oKIydpx9kKXEGM4pkybGwe+pROwCwcfOGQCiCLCXOMO7RlHQ7Tz+D6x2dTgdZShzsPJ5MzU66fQHWrhXg4BVgcOxbO//ErV1/GpRp1SpYSMvnApsd3p2ICUu3Y8LS7Zi4bAdsHJ2RnvBkVkh6QgxsHJ0htXm1RJOLV0FfKvJz9WVarRYov6dhMiMcgJsBZ+cnC4+IRKKCEwCAtLQ0uLk9GSR6eT2ZhpeQkIAxY8YgJCQEISEhmDlzJpKTCy7yBAIBevbsiaSkJISFhf2rumVmZsLT0/NfHePf8G/UFrf2rkN+dgbyszNwa996+Ddq+9zYiLMHkB51D2qlAte2/w7nilVh5+4NlTwPRxdNhVuV2mg+fGqhGe7MuAjodNpCnwsvz+4k50Kp0aJ7TXeIhALUrmCLOl72OB2Z8UrHqetlh8Eh3vjxeJTRM8/min1Dr+JBah5UGh3aV3GBSABUd7NBdXdbXHzm65GUGh2uxuegWw13SMVCuNpYoFUlZ1yIKYgL8rBBr9oeWHEu1uh58MQcBVJzlXizljssRALYSEToWsMNV+OzDaZtlzf3UnKh1GrRuZorRIKCNtb0sMW56EyDOKVGh0tx2ehRq6Bv3GwkaFvZBWcexZ2JzoSvoxTNKjpCAKCWpy2qulnjUlw25GotrsRn442a7rC1FMHKQoiwIDdcic+Gujx3zkvcTJBBqdGiTz1PiIUCBHvbI9jH3uhcIhQAn3esjGy5Gl/uvmeWU6yLQn9eDnKDSChALU9b1KlgV+jXsr1uXCvVgshCgvsHNkCrViH5zkUk37kIn/qtDeLEllJ41WmG27v+hCo/F7KUeDw4th2+DQuuf7zrt9bvq1WrcO/AXxBJpHCtVEt/jPTI23AJqGFUB+eAGog8tQeZMfeh1Wjw4Og2KGRZ8KjZuFTbXlJqNu+A09tWIzczHbmZ6TizbQ1qNu/wysdx9QlAhUrVcXTtr1Dm50GWkYbzuzagetM2pVBr81TWC7BxETYqE66urkhKevK1NE+vSO7q6oqFCxeiZs2aAICsrCzI5QXTcHJzc7Fw4UKEhYXh66+/xubNmyEWv/qfQn5+Pq5cuYJBgwb9y5YUX80uA6FRKrB//gRo1Wr41m+FWl2f1OefOWPg16A1gjr2g2eNBqjdbQhO/zkPClkWXAOD0HRowarx0ZeOITctEdEXMxBz8Zh+fzt3b3SY8iMAIDctCZZ2jmaxEMnTNFodFhyJwDsh3uhaww1Z+WosPR2N+GwFqrpZY3JoAD7bfc/oq4Ke1S3IHSKBABNbVjQo33o9CXvvlu8Fop6HfUOvQqMDlp6JQZ86nmhb2RnZCg3WXo5HkkyJAGcrjGzii28OP0RmvhqbriWiV20PfNYuEDoApyIz9d9l3a6KC0RCAYY18jE4/j93UnD0YQaWnYnBGzU98EX7StDodLiRIDP4nuzySKMDfjkZjf71KqBDVRdky9X480IcEnOUqORijbHN/TBzfzgy8tVYfyUB/ep6YkanytDpgOMRGTgRkQkAiM9W4OeT0ehRywO963ggS67G7+fj9LNSVl6MR4+a7vi0bSAsRULcTJJh/RXznnGi1urw1T/hGNXcDz1qeyIzX4Ufj0YiNlOOGh62mNqpMiZuvglvRymquttCodbiz0FPFhRVaXR4d43x6tbmSqPV4bujkRgS4oUuj87Ly87EID5bgSpu1pjcyh+f77n/0vOyORKKLdBkxHRc27wY4Ye3wNLOCcEDJsHO0w9pD2/izLIZaPPRL7B2ckOdPqNxfetSHJwzChAI4N+sC/ybdgYA2Hv6of7AD3Bz+wrkZabCoYI/Gg//EsKnvkYsLz0Zjr7GCQXPmo0Q1O1dXFg1H6q8HDh4V0Kz92fBspAFzMqjpj0HQ6VUYNWXY6BRq1CtSWs06/WOfvsfn45AjWZt0bj7gBccpUCPSTNxZO0SLP94KLRqNao3aYPmTx2LqLgEuv/CyjlmKDY2Fh07dsSsWbOwfft2/PHHHwCAs2fPYurUqdi/fz/WrFmDDRs2YPny5cjLy8OgQYPQrFkzzJ07FwsWLMD9+/cxb948CAQCTJw4EX5+fpg+fTpmzpyJxMRELFy4EP369UOHDh0watSoF9bnk08+gZ+fn34V9OTkZMyaNQtJSUlYt24dhMKXT6b44p/7/7pfXkcxr+EUQjKNv+YtLesqlGujZ4wr6yqUW897vpgKJPK8/FwONpKXB/1Hudqyb16kiqvx+jz0xMgmFV8eVI60XHCirKuA4x8U/zHassQMuBkbMGAAYmNj0aVLFzg6OqJly5bQPFqQY+zYsfj2228RFhYGhUKB0NBQfPTRR7hy5Qq2bNmCnTt3QigUYtq0aRgyZAg6duyIgICAF/57ixYtwrJlywAAdnZ2CA0NxcyZM4s0+CYiIiIioteDOU8BL2vMgJPJMANeOGbAqbiYAX8xZsCfjxnwF2MG/PmYAX8+ZsBfjBnwFzO3DHir706+PKiUHZvcvKyrUCzMgBMREREREVGRMQFefByAEwBg8uTJOHz4cKHbxowZgxEjRpi4RkRERERERK8XDsAJAPDdd9+VdRWIiIiIiIheaxyAExERERERUZFxEbbi4/LVRERERERERCbADDgREREREREVGRPgxccMOBEREREREZEJcABOREREREREZAKcgk5ERERERERFxkXYio8ZcCIiIiIiIiITYAaciIiIiIiIiowJ8OJjBpyIiIiIiIjIBDgAJyIiIiIiIjIBTkEnIiIiIiKiIhNyDnqxMQNOREREREREZALMgBMREREREVGRMQFefMyAExEREREREZkAB+BEREREREREJsAp6ERERERERFRkAs5BLzZmwImIiIiIiOi1dPHiRXTv3h316tXD0KFDkZycbBSTlpaG8ePHo3HjxmjZsiV++ukn/bazZ88iKCgIwcHB+p9bt24Vuz4cgBMREREREVGRCQVl/1MUcrkcEyZMwNixY3Hu3Dn4+/tjzpw5RnEzZ86Era0tjh07hvXr12PHjh3YuXMnAODu3bvo0aMHLl++rP8JCgoqft8Ve08iIiIiIiKicur06dNwdXVF586dIZFIMGnSJBw4cAAymcwgTiQSYfTo0bC0tIS3tzfat2+Pa9euASgYgFevXr3E6sQBOBEREREREb12oqKiEBgYqH9tb28Pe3t7REVFGcQtWLAAfn5+AACNRoNTp06hWrVqAAoG4IcOHULLli3RqVMnbN269V/ViYuwERERERERUZGVh0XYFi5ciJ9//tmgbNy4cRg/frz+dV5eHqRSqUGMVCpFfn5+ocfUarX48ssvIRKJ0L17dwCAq6sr2rRpgx49euD27dsYOXIkfH19ERISUqx6cwBOREREREREZmX8+PEGg+3CWFlZQalUGpTJ5XLY2NgYxSoUCkyZMgUxMTFYvnw5JBIJAGDJkiX6mHr16qF79+44dOhQsQfgnIJOREREREREr52AgABERkbqX+fk5CArK0s/3fyx3NxcDBs2DDKZDKtWrYKzszMAIDs7G9988w1UKpU+VqVS6QfnxcEMOJnMuYfpZV2FcmlAwwplXYVyzcaCp6nncZ4xrqyrUK4tnvbzy4P+q+xcy7oG5dqsmUPKugrlVlquuqyrUG7lKDRlXYVyLZV/O6+VcjADvUiaNGmCzz77DLt370b79u3xww8/oFWrVkYZ8KlTp0IqlWLJkiWwsLDQl9va2mLv3r1wdHTE8OHDcfnyZezevRvr1q0rdp2YASciIiIiIqLXjlQqxeLFi7F06VI0btwYkZGR+OqrrwAAwcHBuHDhApKTk7F7926cP38ejRo10n/X91dffQWhUIhFixbh6NGjCAkJwaeffoqZM2eiSpUqxa4TU0tERERERERUZAKYSQocQN26dfH3338blV++fFn/+927d5+7f/Xq1bF27doSqw8z4EREREREREQmwAE4ERERERERkQlwCjoREREREREVmdB8ZqCXO8yAExEREREREZkAM+BERERERERUZAJz+R6ycogZcCIiIiIiIiIT4ACciIiIiIiIyAQ4BZ2IiIiIiIiKjDPQi48ZcCIiIiIiIiITYAaciIiIiIiIikzIFHixMQNOREREREREZAIcgBMRERERERGZAKegExERERERUZFxBnrxMQNOREREREREZALMgBMREREREVGRCZgCLzZmwImIiIiIiIhMgANwIiIiIiIiIhPgFHQiIiIiIiIqMs5ALz5mwImIiIiIiIhMgANwIiIiIiIiIhPgFHQiIiIiIiIqMiHnoBcbM+BEREREREREJsAMOBERERERERUZ89/Fxww4ERERERERkQkwA06vjYpOVpjQOgCVXK2RkafC8tMxOPEw3SjOw06C0S38UbOCLbRa4GJsFpaciEK2XA0ACHSxxvstKiLQxRoyhRprL8Zj350UUzenxGnUahxYsxi3Tx+BTqtFjSah6DBkLERiixfud3b3RkRcv4T+H8/RlyVHP8T+VYuQFHkfVnYOaNi5F0I69ijlFpQejVqN3St/wfVTh6DValG7aRuEDR0PcSF9o9PpcGjTn7h05B/Ic3Pg6uWHjm+PQKVa9QEASoUce9f8itsXTkIll6Nq/SZ4c/gkSKRWpm5WsXjYSdC3jie8HaTIUaix81YKriXkGMVZioXoV9cT1dxsoNHpcDoyE//cTdVvb+bviNBAZ9hJRcjIU2PfvVRcjS84jq2lCH1qe6KSqzW0Oh3uJOdiy/UkKNRak7WzLAT6uuL4qimo33sWktKM+/R1VsPfDT9/2B11KnsiKV2GL349gK1HbxnF+Xk64tsJndG8TkXky1X4fdclzPnzKLRaXRnUumRp1Wqc2rAUD84fhU6nRaWQVmjW//3nnoNvHd2FK3s2QC7Lgmflmmg15H+wdXYDAKTFRuDU+iVIjQ6H1NYetdv3RK22b+j3vbp3E24e3gFFngxOFXzRpM9weFapZZJ2FodWo8bVv39D7OVj0Gm18KnXEvV6jYTwOX3z8OQe3D24EYrcbLgEBKHBW+Nh7eRmEKOQZeHQ9x+g0eAP4eJfHQCgUSlxbdtviLt6ChqVEk6+VVCnx3A4egeUehuLy9NOgv7BFeDjIEW2XI3tN5NxJd74/CEVC9E/2BM13G2h0elwMiITu24/uXZ5v6kvqrpZ4+m30jeHI5AiU+pfW4gEGN+iIo6Ep+NSXHaptqukaDVqnNu0FJEXCv52/Bu0RKN+z39f3T22C9f3bYBClg33SjXRbOBE2Di7ISn8Bg788qXhsdUF14WDF243KE8Kv4m9P3yMXjOWw9bFo3QaRq+11zoDHh8fX9ZVKFP/pfaLhQJ8FVYVZyMz0Hv5RfxyLBIftguEh53EKPbzTlWQkafCoJVX8N66q7CViDCxdcGHr7O1Beb3qIHD91PRd8VFzD/4AONa+SPQxdrUTSpxxzf/icSI+xg5bzlGfPMbEiPDcWbnhufGy/NysW/lLzi0dqlBuSIvF3/N/wzufoGY8MtG9P1gJs7u2oDLh3aVdhNKzcGNvyP+4T1M/O5PTFzwB+Ij7uP4tvWFxp7d9zeuHN+PoZ/Px+crtqNRh+5YM+9z5GSkAQC2LJqLjKQEjJ+3HJN+Wo3MlEQc+GuFKZtTbCIBMLyRD24lyfD5nnvYci0JA4IrwMnK+EKmbx2Pgvfd/nAsPBGF+j72aOBjDwCo4W6DrtXdsPpSPD7bfR87byXj7eAK8La3BAD0qu2BPJUG0/eFY+6hh3CyEiOshpvRv/E66dOxPg6umAxnB5uyrorJWYhF2PLN29hz+h4qhM3FpB92Y+mnPeDn6WgQJxQKsPHr/lAqNaje7weEjv4N3VtUx7T32pZNxUvYhR2rkRp9H/2++hX9pv+K1OhwXN27qdDY2JsXcf7vleg49ksM+e4v2Lp44Ogf3wEAlPm52PPTF3DxDcDgBevQedx0XN27CbeP7QYARF87hxuHtqH7h/Pw7g8bUalRa/zz83T9YKI8uvXPWmTGhKPjJ4vR4eNFyIx7gLuHthQam3TnEm7uWYWm701F95lrYOPsjgvrfjSMuXsZh3+cgrz0JMN/Z88aZMVHosPHv+CNr9fBJaAGzv45t9Ta9W+JBAUD55uJMny08y42Xk3EoAZecLY2Pie/Vc8TFkIhvvjnPr4/GokGPvYI8bXXb/dxlGLhiWh8uOOu/ufpwbevoxT/a1kRAc7mcbP4sSs7VyM9Ohw9vvwVb365BGkxD3Bjf+Hvq7hbl3B5xyq0HfUl3pq3HrYuHji56nsAgEflWhj4/Rb9T++Zv8PKwQlNB04wOIYiLwcn/vwWOu3rfcO4KAQCQZn/mKsXDsCHDx+O4OBgBAcHo3r16qhbt67+9YULF0xVx2K5desW3nvvvbKuht706dNRr149TJgw4eXBJeDQoUOYOnVqqRx7z549GDp0aKkcu7jqetvDykKEjZcToNHqcCEmCxeis9AlyN0gTioWIjtfjVXnY6FQayFTaLDrZjJqetoBANpVdcWdJBn23EqBDsDNRBnGbbyBxBxFGbSqZF0/vg/NuveHtb0jbJ1c0LznIFw6uOO58evmTEFeVgaC23U3KI+9fxMqeT7avT0KYokErt4VUb/9G7hy2HwH4JeP7kWrHm/Dxt4Rdk4uaNN7CM7t315obL5Mhja9BsPVyxdCoQgN2nSFyEKChMhw5GSm49a54+gx6gNY2drB2tYe/SdNR5MuvUzcouKp7GoDS7EQh8PTodUBd1JycSdZhiYVHQziJCIB6nrZ45+7qVBqdEjNVeHYg3Q083cEADhIxTgUnoaYTDkA4HZyLhJzlKj46MLOw1YCoeDJB5BOB6g0r+/FzJgBofhidBhmLHr+++11FhrsD1srSyxYexJqjRb7z4Vj/7lwDOtW3yCuiq8L6lT2xP++34WcPAXiUrLx7ZrjePeZOHN179R+1Ov8FqzsHGHt6Iz63d7GraOFnzfvntqPqk3bwdW3EsQSSzTp/R7i711DVlIcEsNvQSXPR5M+IyC2kMDJqyJqtumG28f/AQBkJsZAp9NBp9UA/2fvvqOjqN4Gjn93N9n0Xukl9B5K6C20ANKlijQpUhV8bSBKsSA/GyKIgCAdERCkdwkdKdI7hCSEhPSe7e8fgYUlAUKEFH0+5+Sc7Myd2XsnmXLneeYOmRfJVmqbPGtnbtw+voeKrXti4+iCnYs7ldv24ebhrdmWDTm+h5J1A3EtVhaV2obqnQYTc+McydGZQYeI88c4uWoWVdu/lmXZaq8MoMmb07BxdCEjORFtego2ji5ZyhUU5b0yj8m7r8ZiNGUeSy9GpZiPtQ+oVQr8izmz5VI0WoOJ6FQdf96Io2kZNyDzmOyoVhGemJHt9xR3sWFUoxIcD0skLk33spv1Ql0/uptq7Xph6+SCvYs7NTv05Upw9v8714/uwq9+IO7396s63YYQee0sSffuZCl7ZOVsfMpVp1yD1hbTDy+fRZl6LV5GU8R/yFNT0BcuXGj+PTAwkJkzZ1K3bt2XXqkXITk5GX0Butu7evVq1q9fT5UqVfLk+xISEjC+4LtzRqORZcuW8dVXX1GnTp0Xuu5/qqSbHWEJGTyapBgWn06ZxyLXGXojH225YjGtYRk3rkWnAlDB24G7SRo+aO1H7RIuxKfpWPZXOKHx6S+7CS9VRmoKKQlxeBQrZZ7mUaQEKfGxpKckYefonGWZV8dPw8ndkwPrlpJw7655usloQmWtRqlSmacplSri7mY9gRUG6akpJMfH4l28tHmaV7GSJMfHkJaShP1j26Zlj9ctPodcPocmPRXfUn5E3LqKo6s7F48f5NDWtei1GqoGNKNd/xF50ZR/zMdJzb0UrcV+dC9FSxFny4t3Twc1KqWCqEduTEWlaCnilFnuaGiiRXkPe2t8ndSE3++Q77kWR8+avtQt4YJSoeBWXBrbLsfwb7V+12l+XB1MCV+3/K5KvqhU2ouroTGYTA//s66ExlDNzzJ1U6lQYDSaSNc+PHcbDCa83Rxwc7IjPrnwHoc1aSmkJcbhVrSkeZqrbwnSEmLJSE3G1sHJonzC3TCKVapl/qy2d8De2Y24OyGorKxRWVtbHIMVSiWJUeEA+NVrzuVDO1j90RsolEqsrG1o/9Z0lFYF86lDbXoKGUlxOPuUME9z8i5ORmIc2tRk1I9tm+SoMLwr1DB/trZzwNbJjaS7ITh5FcWjdCWCPlqA0sqa48u+slhWoVRhpVZxdd96zm36BWsbe5q8Oe3lNvAf8HVSE/XYMTkqWUsxF8tjspdj5jH50WBBVLLGfOwu4WpLht7IsPrFKe5qS0K6nq2XojkfmQJATKqOT3ZcR2sw0dLP/aW360XRpqWQnhiHq+/D/crFpwTpibFoUpOxeex/JzEyjCIVa5o/q+0csHN2I/5OCM7exczT71w8ReSVs3Sf9rPF8peDt2DQ6ajcvBPntv/6klpVeCgLbwA63+UqBT0kJIRBgwZRr149evTowdmzZwE4duwYffv2ZdKkSfj7+9OtWzdOnz5Nv3798Pf3Z9y4cRiNRsLDw2nWrBkzZszA39+fV155hVOnTpnXHxwcTKdOnQgICGDUqFHExmamds6ePZvRo0cTGBhI3759Afjpp58ICgrC39+fDh06cPz4cdLS0hg2bBhhYWE0btwYgIoVKxIZGWn+jsDAQE6cOGGuy+jRowkICODixYvExMQwZswY6tevT8eOHQkODs7RdtmzZw8dO3akbt26DBo0iJCQEAAaN26MyWSiX79+bN2a/V25BxYuXEiTJk1o3LgxY8aMISEhgaNHj9K8eXOLi5dOnTpx7NgxDhw4QFBQEAEBAfTp04cLFy5w48YNPvnkE44fP86rr776Qv5mAHPnzmXz5s0FKrPgAVtrZZbnRzV6I7ZWT/8X71mrCE3KujP/0G0AnGysaFfJiwM34+i75DQ/Hw3jvdblKO9VuNNGtRmZF67WNg9P2g9+12myj+47uXtmO714xWooFAoO/r4cvU5LzJ3bnN67Gb1Om235gs68bR6JED34/Unb5oHI2zdY/e0UWvd+A2d3T9JTkklJiCP8+iVGz5jPm5/9SNi1i+xcueDlNeAFUquU6AyWz9pqDSbUKsv9yMZKid5osniWUGcwYa3Kur+52lkxtH5xjoclEnq/A65QwOGQeCZtu8bUndcxmTLT0v+tImOSLI7f/zUOdmrSNJZRtbQMHfY2lmm0V0JjuBQSzRej2uJgp6a4tzNv92kEgK26YHYec0qnyTzOPBqJfvC7Xps1KqnTpGeJWlupbdFrNfiWq4pCoeTU5pUYdFri74ZyKXgbBl3mNjbodXiU8KPH5DkMnv07tTr0Zte8z8hIKZjP9Oo1me1XPdLeB7/rdVmPwXptBipry22jUttg0GaWtXF0eeKz4w/4NXmFrjPXUa55Fw7O/wRtasEck8HGSon2sWsbncGYo2Pyo8duK6WC23HpbDh/j4+2XWPX1RiGBBSjhKstkBmc0BoK3zFKd/9/J/v9Kpv/nWz3K5ssZc9sXUGV1t0sOvDxEbe5sGstjQeMf2H1F/9dz90B1+v1jBw5kmbNmnH48GFGjhzJyJEjSUnJvIt26tQp6tWrx4kTJ3B3d2f48OFMnTqVXbt2cerUKY4cOQJAVFQUGo2GY8eO0b9/f8aOHUtGRgZhYWFMmDCByZMnc/DgQfz8/HjvvffM3//XX3+xcuVKFixYwLFjx1izZg3Lli3j5MmTtG3blq+//hp7e3sWLFhAiRIlOHTo0DPbFBUVRaNGjdi3bx+VKlXi3XffpUiRIhw4cIDp06fz3nvvcefO06N7N2/e5IMPPuDjjz/myJEjNGzYkJEjR6LT6cx12L59Ox06dHjiOkJCQliyZAl//PEH+/btQ6/Xs2HDBgICAtDr9eZOc0hICHFxcdSrV4+JEyfyxRdfcPz4cVq2bMmcOXPw8/Nj6tSpBAQEsHbt2hf2N+vVqxe//fYbJUuWfGIb8kuGzoiNyvJWnI2VknSdIdvy1ioF7wSWpUsNH97deNGcKqs1GjlzJ4lDN+MxGE0cv53A3+GJNCxT+CJX2xd9x1dvdOKrNzox/70hAOi1DzvJDzqXzzs4mK29A73e/ZyQC6eYPbYP2xfPolqTNtg6OL64yr9kGxd8w7QB7Zk2oD3fjR8AgO6RE/CD35+2bc4f+ZMFn7xFk1d60axL5g1BKytrjEYjQf3fxNbeARcPL5p26cuF4zm7iZfftAYj1o/tR2qVIsvNLa3eiJVSYXH321qlQPtYGrmfhx3jm5Xm8r1U1p3NfBbTyUZF71pF2H0tFo3eSGJG5kBv9Uq4YCW30/+V0tK12D3Wgba3tSY13fKmndFo4tUPV1Hc25nLv77N6k/7sGbPOQASUrJPnS3IDiyfzaKx3Vg0thtrJg8DMHcS4WEHwdom63HGysY2y01NvTYDaxtb1PYOtB83jTuXTrP8vf4cXD6bCg1aYWOfeQw+vHoe3qUr4FGiLFbWavzb90Zta8+t08++Hsorp9bMYcP7Pdnwfk92fvEmkDlA2gMPtpOVjW2WZa3UNhZlH5RXZVP2SVTWalRW1lRu1weFQkn0jXO5acZLp9VnvQFqrcoacMjumPzosfvviGR+PBJGRJIGowlO30nmSnQqNYpYRogLgyMrZ7NifHdWjO/OhqmZ+9Wj+8rD/Sq7/x1bDNrH9yuNRdmEu6HE3r5GxSYdLMocWPQl9fuMws7J9UU2R/xHPfct5bNnz6LRaBgyJPOCvnXr1ixZsoTg4GA8PDxwcXGha9euAPj7+2Nra0v58uUBqFChAnfv3qVUqVIolUreeecd1Go1ffr0YcGCBZw6dYqzZ8/SqlUrAgICABg7dix16tQhJiYzPbFWrVr4+voCUKNGDVauXImnpyeRkZHY29sTHZ270arbt2+Pg4MDUVFR/PXXX8ybNw+1Wk3t2rVp2bIlW7ZsYfjw4U9cfteuXQQGBlK/fn0Ahg8fzooVKzh//jz+/v45qoOdnR3JycmsW7eOtm3b8uOPP5oHGGjXrh07d+6kZs2abN++nbZt26JUKnFycmL9+vUolUqGDh2K6pGUtAdexN8MwNvbO8u6C4rb8en0cS1qMa2Em122qePOtlZ82rEiBpOJsb9dID79YWQmLD6DKr6WHUmlQlEo33UYNORtgoa8bf48e0xvYiPCcPPJ3E6xd8NwdHV/7o6zXqfFaDDQ/6NvzNP+/PVnfMuUfyH1zgtdhk2gy7AJ5s9fvvkqMRGhePhmpqBF3wnFyc0Duydsm71rl3B4y2/0HDuRSnUamad73U/xz0hLxdE1M43PZDRkPuRcCEQma2hV3sNimrej2iLVHCA6VYvBaMLLUU1UcubFjI+j2iL9sX5JF7pW82Hj+SiLlHRnGyuslApUjwyeYjCZMAHGQrKdxPO5GBLN//VvajGtYklPLoVYnq8VCgVuTrb0nvQrOn3mzdM3OtfhUkg06ZrC9VwqQNP+Y2naf6z58/J3XyMhMhxn78xjcEJkGPYu7uaO86PcipYiITLM/FmblkpaYjyuRUti0GkxGg10evd/5vnH1y/Gs1Q5AFLiozEaLG8+K1UqVKqCk0VQu9doavcabf685ZOBJEeF4+hZBIDke+HYOrujtsu6bZx9S5J8L9z8WZeeSnpSPM4+zw4OHF/+NR6lK+HXpGPmBJMRk8GAtV3BzHK7m6yhjZPlMdnHSZ1lXJp7KZnHZG9HNZEPjslONuZydYo7YzCaLEZPt1Iq0BfCgcQa9htLw34P96s1H/YnKTIMZ6/M/53EqDDsXNxRZ7NfuRYpaX5UAzIHNExPjMelyMP/ndAzRyhSqRa2Tg/HBogNvUZSdATBi2ZmTrh/rvrjs1E06DuGsvVavtA2FhaFeRC0/PbcEfDIyEgiIyOpW7eu+efcuXPmTpqz88PnJR90EB9QKBTmNDwXFxccHR/uHD4+PsTGxnL37l22bt1qXnejRo1QKBTmEb09PS3TYr/66isaNGjA6NGjOXPmzPM2BwCVSoW7u7u5fXq9nsaNG5vrsH37dqKiop66joiICIoUKWLRVl9f32cu9ygfHx9mzZpFcHAwHTt2pEuXLly4cAHIvEGwe/duAHbu3En79u0B+OGHH0hMTGTw4ME0b96cjRs3Zlnvi/qbFWRn7yShMRjpW6coVkoFdUu4UK+kC3uuxlqUUypgWoeKJGboeW/DJYvON8Cuy9GU83KgXWUvFEBAKVdqFnPmwI2srzMrbKo3bcvBDctJSYgjJSGOQxtWUL1p2+dej8lkYvWM97h4NPN1ZrcvnuH0ns3UbdvtJdQ6b/g3a8e+dUtJTogjOSGOP9cvxb9Zu2zLHt3xO4e3rmXo1O8tOt8APiXLULxcZbYtm4smPY2kuBiCN66mRuNWedGMf+xGTBo6g4nW5T1QKaCSlwOVvB05GW6Zuqo1mDgTkcwrlb2xtVLi6WBNMz93ToRllqvi40D36j4sOh6e5XnwyGQNMalaulTzxlqlwEGtokNlL85EJPEveNOUyEbw6RAyNDreH9AMaysVbQLK0bZ+OVbtPGtRzmQyseSTVxnbswEKhYIa5Xx5//VmzF17LJ9q/mJVaNiak5tXkpYYR1piHKc2r6JCw9ZPLHvl0C7uhVxBr9VwdN3PeJepiKtPcUwmE1u+mZj5OjOjkYgrZ7kYvJVqgV0AKFWjPuf2bCA+4jZGg4EL+zaRlhRP8WoFdwyfUvUCubRzNRlJ8WQkxXNp56+Uqpf96Pcl67Xi9vHdxIVexaDVcG7TYtxLVcDpkWd4n8S9ZAWu7l1HSnRE5ivJ/liMrYs7HmXyZnye53UtOhWdwUS7ipnH5MreDlTxceSvMMvjqtZg4u87SXSumnlM9nKwpoWfO8fvH39trJT0rOlLUWcbFEC9Es6Ucbfnr7CC+VjC8/Cr34oz21aSnhhHemIcZ7etwq9+9udcvwatuX5kFzEhV9FrNZz8fRGepSvi4lPcXCb65kW8/apaLOdTrhr9Z22g39e/0e/r3+j2SebbYTpPmvuf7XyLf+a5b4d6enri5+fHpk0PR3MNDw/Hzc2N8+fP5/huSHJyMlqtFrU68zVRkZGR+Pj44OXlRe/evS1G8L5x4walSpVi//79FutfvHgxCQkJBAcHY2Njw759+5g+fXq236dQKMyDsplMJhITE7Mt5+npiaOjI3/99Zf5ux5E15/G29ub0NBQ82ej0cjdu3fx8PB4ylKW4uLicHd3Z9myZSQnJzN79mymTJnCb7/9Rp06dUhLS+PgwYPcu3ePunXrotVqiYqK4vvvv0en07Fjxw7ef/99WrWyPPC8qL9ZQaY3mpi0+TJjm5Whp39R4tO0zNxzg9D4dKoVceLTVyoybNVZSrjZUdnXEY3eyG9vPBxITqs30mvxKULi0pm06TJvNCzJiMaliEvV8uXu69yMTcvH1r0YTbq/jk6TwaJJIzEYdFRp0IKmPQaa5y94fyhVGwXSqEu/p67HWm1D97ensHv5j2xd8DXOHt60HTQWv5r1XnYTXprAngPRaTOY8/4wDHo9NRq1pFWvhyP9f//OIGo0aU2Lbv3Z//sKtBnpzP9otMU6eoz+gKr1m/P6+1+wdekcvn2rPwaDgRqNA2nde0heNylXDCaYfzSMV2v4EljOnSSNgZWnI4hK0VLG3Y7hDUrw5b6bJKTrWXs2ku7VfZjYqiwm4HBIAkduJwDQqrwHKqWCIQHFLda//XI0+2/Gs+BoGJ2r+jC5tR8Gk4nzd1PYfDF32Uui4NPpDXR+dznfT3iFCX0bExWXwhuf/c7l29E0rlGSDTP7U3vAHMLuJdL/k9+YNaEjHwxsRmxiGt+vOcLCPwr2W1dyqk6n19BrNaybPgajQY9fvebU7fxwUMffPhlBufot8e/QhxJV6xDQbSB75s/IfA+4X1Vaj5gIZKZgtx01mSO//sT+pd/h6O5Fk76jKHG/g13nldcwmUxs+/5jtOmpuBcvQ4e3P8PeueA+SlU5qB96rYY9X72F0aCnuH9TqjwyivnOGaMoWacFldr0wrdSbap2GMDxpf9Dk5KIR5nKNBj4QY6+x6/pK+gyUgmeOwmDVoNH2So0GTHlie+Mzm8GE8w9HErvmr60Ku9BskbPspMRRCZr8fOwY2Sjkny2+wbx6Xp+PRPJqzV8+bitHyYTHLwVz6GQBCDz+OygVjGiYQkc1CqikjXMOxJW6EY8z06tjv05qdWw6YuxGA16Stdphn+nh/vVhulvUrZeS2oE9aZYlTrU7jKQ/YtmoElJxLtsFZoP+9BifcmxUZTyb5LXzSiU/gXdh3zz3B3wWrVqkZqayrp16+jWrRvnz59n8ODBLFr0fO+51ev1zJkzhzFjxrB+/XpMJhO1atXCzc2NAQMG0LlzZ6pXr86vv/7Kt99+y59//pllHSkpKVhbW6NUKomKiuK7775Dd38QErVaTXp6OgaDAZVKRalSpdi8eTNDhw7ll19+ITU1Ndt6FStWjHLlyjFnzhxGjBjBnTt3GDhwIO+///5Tn98OCgqiZ8+edO/endq1a/Pzzz9jZWVFzZo1n7jM48LDwxk+fDirVq2idOnSODo6mqPTSqWSdu3a8eWXX9KuXTuUyszkhXHjxvH555/Tpk0bPDw8sLW1Ra1Wo1arzc94v6i/WUEXFp/BexsvZZl+/m4yXRdkXsBFp2hpN/fp0ZSzEcm8te7CS6ljfrKyVtNmwGjaDBid7fxhXy7MdnrTHgOyTCtd1Z+hX8zPpnThZGWtpuOgsXQcNDbb+eO+/sX8+/vzsn+/6AMOzi70HDPxRVYvT91L0TL3cGiW6bfi0vlw61Xz5wy9kZWn72YpBzD7YNblHxWdquPn4+FPLfNvFHo3Djv/MfldjXxx5XYM7d76Jcv0Q2dD8Qr63Pz57PVIWo76OUu5fwOVtZpGfd6kUZ83s53fc+pPFp8rN+tA5WbZX3cUq1SLVz/5Mdt5SisrAroNIqDboH9U37yksrKmVvfh1Oqe/aN+bT+Ya/G5bKMgyjYKeuZ6e3xr+eo/hUJB5bZ9qNy2T+4rm8eikrV8n80x9UZsOv+36eFbXdJ1RpadjHjienZdjWXXY1mB2Zmy80buKppPVNbWBPR6k4Be2e9XXSfPs/hcoUl7KjRp/8T1PV4+O3Yu7gyc+/RBlYV4mudOQVer1cybN888ONjbb7/Nhx9++FwdTchM+05NTaVx48asWbOGH3/8EbVaTfny5Zk2bRoTJ06kTp06rFmzhnnz5mFnl3WQkkGDBpGYmEhAQAB9+/alY8eOJCQkkJiYSPny5fHy8qJ+/fpoNBqmTJnCxo0bqV+/PmFhYdSu/eT3in777becP3+eJk2a0L9/f/r27fvUzjeAn58fM2fOZNq0aQQEBHDw4EEWLFhgjvDnRI0aNRg+fDgDBw6kdu3aHD9+nI8//tg8v0OHDly9etWcfq5Wq/n222/57rvv8Pf3Z8qUKXzzzTeo1Wrq1q1LQkIC7dq1e2F/MyGEEEIIIYQQuacw5cMDvuHh4bRt25aLFy/m9VcXahEREfTp0ydLKn5h8azI839V33pFnl3oP8zBuuAMHFTQHAkt/M/vvUw/fvJDfleh4HLK/jWDItOn07Nm/ohMsan6Zxf6j0rWZP/mFZGpiHPOg1L/RRNb+eV3FZ7LgJVnn13oJVvar0Z+VyFX5Mq2kLh27Rq//PILXbp0KZSdbyGEEEIIIYT4r5MOeA4lJyfTrFmzJ87fvn07Pj4+z1xPnz59uHLlSrbzPvvssyemuo8ZMwZ7e3sWL16cswoLIYQQQgghxEuglHhgruVLB7x48eKFLv3cycmJ06dP/+P1rF69OlfL7dix4x9/txBCCCGEEEKI/PPcg7AJIYQQQgghhBDi+UkKuhBCCCGEEEKIHJMxqXJPIuBCCCGEEEIIIUQekAi4EEIIIYQQQogck/h37kkEXAghhBBCCCGEyAPSARdCCCGEEEIIIfKApKALIYQQQgghhMgxpQzClmsSARdCCCGEEEIIIfKAdMCFEEIIIYQQQog8ICnoQgghhBBCCCFyTDLQc08i4EIIIYQQQgghRB6QCLgQQgghhBBCiBxTSAg81yQCLoQQQgghhBBC5AHpgAshhBBCCCGEEHlAUtCFEEIIIYQQQuSYZKDnnkTAhRBCCCGEEEKIPCARcCGEEEIIIYQQOaaUEHiuSQRcCCGEEEIIIYTIA9IBF0IIIYQQQggh8oCkoAshhBBCCCGEyDHJQM89iYALIYQQQgghhBB5QCLgQgghhBBCCCFyTCEh8FyTCLgQQgghhBBCCJEHJAIu8oydWpXfVSiQMvTG/K5CgeZgnd81KLjkf+cZnDzzuwYFV3JMftegQNPoTfldhQKrjIdNflehwNrwd1R+V6FA83Zyz+8qCFEgSAdcCCGEEEIIIUSOSRp17sm2E0IIIYQQQggh8oBEwIUQQgghhBBC5JgMwpZ7EgEXQgghhBBCCCHygHTAhRBCCCGEEEKIPCAp6EIIIYQQQgghckwpGei5JhFwIYQQQgghhBAiD0gEXAghhBBCCCFEjkkEPPckAi6EEEIIIYQQQuQB6YALIYQQQgghhBB5QFLQhRBCCCGEEELkmLwHPPckAi6EEEIIIYQQQuQB6YALIYQQQgghhBB5QDrgQgghhBBCCCFyTKnI/5+cOnnyJJ06daJWrVoMHjyYe/fuZSmTlpbGW2+9Re3atQkMDGT37t3medHR0QwZMgR/f386dOjAyZMn/9m2+0dLCyGEEEIIIYQQBVBGRgbjxo1j9OjRHD9+nNKlS/PFF19kKffNN99gMpk4fPgwn332GR9++CEJCQkATJ48mXLlynHs2DHefPNNxo8fj16vz3WdpAMuhBBCCCGEECLHFIr8/8mJI0eO4OnpSVBQEGq1mvHjx7N7925SUlIsym3evJmRI0dia2tLw4YNqV+/Plu3biUlJYXg4GBGjx6NWq2mc+fOeHh4cOjQoVxvO+mACyGEEEIIIYT417l9+zZly5Y1f3Z2dsbZ2Znbt2+bpyUmJhIfH29RrnTp0ty8eZPQ0FDc3NxwcXHJMi+3pAMuhBBCCCGEEKJQmT17NhUrVrT4mT17tkWZtLQ0bG1tLabZ2tqSnp5u/pyeno5CocDGxiZLmZws/7zkPeBCCCGEEEIIIXJMWQDeAz527FjGjh371DJ2dnZotVqLaRkZGTg4OJg/29raYjKZ0Gq1qNVqcxl7e3vs7OzQaDRZlre3t891vSUCLoQQQgghhBDiX6dMmTKEhISYPycnJ5OYmEjJkiXN01xdXXFzc7NISw8JCaFs2bKUKlWKhIQEi2fGH8zLLemACyGEEEIIIYTIMWUB+MmJBg0acPfuXbZu3YpWq+W7776jWbNmFhFwgA4dOjB79mzS09M5duwYR48epVWrVjg6OtK4cWNmzZqFVqtly5YtREdHU79+/effaPdJB1wIIYQQQgghxL+Ora0tP/74I/Pnz6d+/fqEhIQwbdo0APz9/Tlx4gQAEyZMQK1W07x5cyZNmsTMmTPx9vYG4LPPPuP27ds0bNiQefPmMXv2bIvnxZ+XPAMuhBBCCCGEEOJfqWbNmmzYsCHL9NOnT5t/d3R05Kuvvsp2eU9PT+bPn//C6iMdcCGEEEIIIYQQOVYAxmArtCQFXQghhBBCCCGEyAMSARdCCCGEEEIIkWMF4TVkhZV0wMW/RglXW0Y2KUUZD3sS0nQs/esOR0Lis5TzdlTzRsMSVPZxwmgy8fedJH4+EkayRm9RrmFpV/rVLcbYtRfyqgkvlUGvZ/+qeVw5th+T0UiFgGa07D8KlZX1U5c7uW0tty+covv/fW6eduX4frbNm4GVtdo8rXZQDxp1G/DS6v8yGfR6ti6dw7nDezEajVRv2JKOg8dilc22MZlM7F27hFN/bicjNRnPoiVp228YftVqA3DuyD5++/5Ti23T+JVetOo1OM/a808UcbKhr38RirvakpShZ+P5KE5HJGcpZ2ulpJ9/Ear4OGIwmjgQEs/mi9Hm+cVcbOhZw5fiLrak6QxsuxzDkdsJACgV0L6SF/VLumBvreJ6bBorT0WQpDHkVTNfisqlvfjh/zpRo5wvUXEpTP5pN7/vv5ilXElfV74aF0TjGqVIz9CxeMspvliyH6PRlA+1LhjKlvDkwLJ3qd3jU6Jis/6//RsYDXqOr51PyIlgTEYjpes0JaDXm088Bl8J3sK5nWvQpCTh7VeVRq+9hYO7F1HXz7N7zseW69Znnr9en/0HBp2Wv9Yu4Pbpg+h1WjxLlafeq8NxL577V+bkNYNez76V87hy9E+MRiOV6jcn8PVnn6/+2raW2+dP8eq7meer8CvnWPfVpCzrBpiweOvLqfxLVtLNjjFNS1PW0574NB2/HAvj0K3sr3VGNC5FFd/Ma53T4YnMPxxKUobltU5RZxtm9ajGtO1XOXe38O17RoOeE2sXcPtkMCajgVJ1mlG354gn/q9cPbCVCzvWkJGauV81eG0cDm5eANw+eYCDv8xEZfXw/F25VTdqvtIfgE3T3yQl9h6KRzqePWeuRmX99P9LIR73r+6AR0REULRo0fyuRr75L7XfSqngo3bl2XEpmslbrlKjmBPvtfLjRkwq91K0FmXfbeXHrdg0hq46i7VKwdstyjCqaSm+3H3DvK7uNX3pWasIUcma/GjOS3Fkw1LuhVxn4OcLMBoN/DFrCie2/kb9zv2yLa9JS+Xw+iX8vXsjparVsZh3L+Q6VZu0pc2Q8XlR9Zduz2+Libh5lbe+WYLRYGD5/z7iwMbVtOzxepayx3Zu4O8Duxg86X+4+xbl9P4drJg5ifGzluPk5kHErWv4t2hPtxH/lw8t+WdUChjZqAQHb8Xz3YEQKnk7MLR+CW7vvkFcms6ibF//IlirFEzadhVnW2vGNC7JvWQtx8MScba1YnzT0vx+PopZB25T1sOOsU1KEZaQTniihi5Vvang5cDX+0NI0xkYWKcYffyLMP9oeD61/J+ztlKx/st+/PzHSdq99Qst65Rl5bRenLwSQWhkgrmcUqngt8/7cC00lkq9vsPZwYZ1M/qhtlLxyYI9+deAfPRq29r8791XcXdxeHbhQuzvzcuJC71O149/wmg0sHfeNM7vWkvN9n2zlL1z8RSnNy2j7bjPcPYpzl9r53No2be0fetzfMpV47Vv15vLZqQksnnGOGq9knm8Or1pGfERt+jy8TzU9o6c2bKC/Qs/p9uUhXnW1n/q8O9Libp1jcEzFmI0Gtjw3RSOb1lDwy6vZVtek5bKwXW/cHrXRkpXr2ueXrxidd5a8If5c1pyIss+Hk3j7oXzZrGVUsEnQRXYfukeH26+TK1iznzYphzXos9ludb5sE05bsamMWjF31grFbwT6MfYZqX5bOd1i/W928oPW+vC+0Tqmc0riAu7TqfJ8zAaDez/aToXd62jevs+WcpGXDrFmU3LaDX2U5x9inNi7XyOLPuW1uMyb9jEhV3Hr0EbGrw2Lsuyek0GSVF36PbpYuxdPV96u8S/21P3uKFDh+Lv74+/vz+VKlWiZs2a5s8PhmwvqC5evMgbb7yR39UwmzJlCrVq1WLcuKw79cuwd+9ePvrooxe6zrNnz9KrVy9q165NUFAQO3fufKHr/yeqFXHCzlrJ72cjMZhMnA5P4nR4Im0qeVmUs7VSkpShZ9XJCLQGI6laAzsvR1PZx9Fc5p3AslTxdeL3s5F53YyX6uLBXdR7pTf2zq44unrQoMtrnNm7+Ynl1858n7SkeGoEvpJl3r3b1/ApU+FlVjdPnd6/g2Zd++Hg7IqTmwctewzg+K4/si2bnpJCy+6v41m0BEqlijotO6CyVnM3JPOiJuLmVYqVLZzbpoKXA7ZWSnZdjcVogotRqVyMSqFJaVeLcmqVgtrFnNl8MRqNwUR0qpa912NpWtYNgPolXAiJT+dQSAIm4EZsOjP23iQmVYdSAU3KuLHmTCSJGXp0BhMrTkew8fy9vG/wC9TcvzSOdjZ8vfIQeoORXcevs+v4dYa8UtuiXPkSHtQo58vb324hOU3DnegkvlpxgEGPlfuvGNW3OZNHdmTq3E35XZWX7vrR3VRr1wtbJxfsXdyp2aEvV4Kzj8JeP7oLv/qBuJfww0ptQ51uQ4i8dpake3eylD2ycjY+5apTrkFrAGp3HUSbsZ9i6+hCRnIimrQUbJ1cX2bTXrjzB3ZSv1Mf8/mq4TPOV2tmvEdaYgI1szlfPWrX4lmUqFSDak3bvugq54kaRZ2xs1ay9u+7GIwmToYlcjIskaDK3hblHlzrLD9xB43eSIrWwLZL96js42RRblD9Epy/m4zeUHizb24e203Vtj3N+1X19n25eiD7/erGkV2UeWS/qt1tCFHXzpn3q9iw67iXLJ/tsnHhN7FxcpHO9yMUivz/KayeGgFfuPDh3dLAwEBmzpxJ3bp1n7JEwZGcnIxer392wTyyevVq1q9fT5UqVfLk+xISEjAajS9sfQaDgTFjxjB27Fh69OjBsWPHGDVqFFWqVKF48eIv7Htyq4SrLeEJGTx6CglPyKC0u71FuQy9kek7rllMq1fSlRsxaebPCw6HEpemI7C8x8uscp7KSE0hNSEOj6IlzdPci5QgNSGWjJQkbB2dsyzT5a0pOLp5cuT3ZSTeu2sx797tGyiVKo5tWolCoaRCvWY06j4QK7U6y3oKuvTUFJLjY/EuXto8zatYSZLjY0hLScL+sW3zeFQ85PI5NOmp+JbyA+BuyDWUKhV//r4chUJJ9YYtaNVrCNaFYNv4OtkQmay12I+ikjUUc7G1KOftqEalVHD3kQyRyGQtRZ0z34lZ0s2WmFQtg+sVo7K3A0kZejZfiiYyWYuvkxo7axUe9tb0r10UB7WKS/dS+O1M4b7hVam0F1dDYzCZHm69K6ExVPPzsSinVCgwGk2kax+enwwGE95uDrg52RGfnJ5ndS4I1u86zY+rgynh65bfVXmptGkppCfG4er78Bjs4lOC9MRYNKnJ2DhYdowSI8MoUrGm+bPazgE7Zzfi74Tg7F3MPP3OxVNEXjlL92k/m6cplSqUahXnd63j5IZFWNva02bspy+xdS+W+XxV7JHzVdESpMTHkp6ShF0256uub0/Fyd2TQ+uXkhid/bEk5NwJQi/+zdCvfnlZVX/pSrhld62TTplsrnU+2XbVYlr9Uq5cj0k1f65TwoXqRZx4Z8NFXqlqeZwqLLLfr4o/cb9KigzDt2It8+cH+1VCROZ+FR92E6VSxfntq1EoFJSs3ZRanV5HZa0mLuw6Kms1O77+P5Ki7uDsUxz/roPx9sub63rx75KrnJOQkBAGDRpEvXr16NGjB2fPngXg2LFj9O3bl0mTJuHv70+3bt04ffo0/fr1w9/fn3HjxmE0GgkPD6dZs2bMmDEDf39/XnnlFU6dOmVef3BwMJ06dSIgIIBRo0YRGxsLwOzZsxk9ejSBgYH07ZuZsvXTTz8RFBSEv78/HTp04Pjx46SlpTFs2DDCwsJo3LgxABUrViQy8uFBOTAwkBMnTpjrMnr0aAICArh48SIxMTGMGTOG+vXr07FjR4KDg3O0Xfbs2UPHjh2pW7cugwYNIiQkBIDGjRtjMpno168fW7c+/ZmjhQsX0qRJExo3bsyYMWNISEjg6NGjNG/e3OLCrlOnThw7dowDBw4QFBREQEAAffr04cKFC9y4cYNPPvmE48eP8+qrr76Qv1lMTAw1a9akZ8+eKJVKGjZsSJkyZbh8+XKOts3LZmutQqu3vOGg1RuxsXr6v3i3Gr40LO3GoqNh5mmPp9r+G+g0mRf1VuqHHakHv+u02afZO7plf5c3IzUFj2KlqBDQnMEzFtHj3S+4feEkB377OdvyBZ02I3PbWKttzNMe/K7TPP0RhMjbN1j97RRa934DZ3dP0lNT8C5emuoNWzL+u2UMnvQ/rp/5i50rf3p5DXiBbKyU6AyP7UcGE2qVIks5vdHEo48s6wxG1KrM/c1BraJhKVdO30nig61X2XDhHoPqFaOkqy0OahVGk4n6JV359kAI03Zdx0Gt4vU6hftxGQc7NWkay2NHWoYOexvLZwOvhMZwKSSaL0a1xcFOTXFvZ97u0wgAW/W/+qmwbEXGJFmc2/6tdJoMAKweOc48+F2fzTFYr0m3KPug/ONlz2xdQZXW3bJ0NAAqt+hE/+82UCWwK7t/mIwmtXA83/vgfGX9yPnqwe/ZbSsAJ/dnRyUPb1hO3fY9su3AFxa2Vio0j13raHJwrdOjpi+Ny7iz8EgoAG521oxuWpqv9t5AX4jHnniwX6ke2VdUT9mvdJqMLPuV6v5+pU1LwaVISUrVbkrnT+bTauxn3L18mlMbFpvLepQsR6OB79D9s6WU9G/MvrkfkxpXuLO3/gmlIv9/Cqvn7oDr9XpGjhxJs2bNOHz4MCNHjmTkyJGkpKQAcOrUKerVq8eJEydwd3dn+PDhTJ06lV27dnHq1CmOHDkCQFRUFBqNhmPHjtG/f3/Gjh1LRkYGYWFhTJgwgcmTJ3Pw4EH8/Px47733zN//119/sXLlShYsWMCxY8dYs2YNy5Yt4+TJk7Rt25avv/4ae3t7FixYQIkSJTh06NAz2xQVFUWjRo3Yt28flSpV4t1336VIkSIcOHCA6dOn895773HnTta0r0fdvHmTDz74gI8//pgjR47QsGFDRo4ciU6nM9dh+/btdOjQ4YnrCAkJYcmSJfzxxx/s27cPvV7Phg0bCAgIQK/XmzvNISEhxMXFUa9ePSZOnMgXX3zB8ePHadmyJXPmzMHPz4+pU6cSEBDA2rVrX8jfzMfHh9mzZ5vrGhkZyc2bN6lQoWCk2mbojagfOwGprZRk6LIf1MlapWBcs9J0rOLNR1uvcCcxIy+qmad2/zKLH0Z04YcRXfjlg8zHMfS6hyckvTazzdY2ds+1XlsHR3p9+BVVm7bFSm2Dm29x6nd+jesnDr64yr9kGxd8w7QB7Zk2oD3fjc98FvDRGxEPflfbPnnbnD/yJws+eYsmr/SiWZfMG4J2Do4MnTKL2i2CsFbb4Fm0BC16DODCsZzdxMtvWoMRa9Vj+5FKkc0FnwkrpcLi5GetUprL6Y0mrkan8XdEMkYTnI9M4cq9VGoUdUJvNKFUKNhyKZoUjYE0nZHNF6Op5uuEVSE+m6ala7F7rANtb2tNarrlc5lGo4lXP1xFcW9nLv/6Nqs/7cOaPecASEj59x2H/suOrJzNivHdWTG+OxumDgNAr3v4//Cgg2BtY5tlWSu1LQat5f+OXquxKJtwN5TY29eo2CT76wqVtRqVtTU1O/RDoVQSefXsP27Ty7Jr8SxmDevMrGGd+fm9IYBlB0qXy/PVA7F3Qom8eZWaLTv+88rmI43ekOVax8ZKSbou+4xHa5WC8S3K0KmaDx9sukR4QuZ2nNCyLL+djiAsofAdc46t+oHV43uwenwP/pg2HADDI/uV4Wn7lY0tBp1lx9yg1WBtY4fa3pG247/Er2EbrNQ2OPsUo3pQH8L+zryGr9SiM82GTsTJswgqa2sqB3bF3s2biEunX1ZTxb/Yc99uP3v2LBqNhiFDMg+QrVu3ZsmSJQQHB+Ph4YGLiwtdu3YFwN/fH1tbW8qXz3yeokKFCty9e5dSpUqhVCp55513UKvV9OnThwULFnDq1CnOnj1Lq1atCAgIAGDs2LHUqVOHmJgYAGrVqoWvry8ANWrUYOXKlXh6ehIZGYm9vT3R0dHkRvv27XFwcCAqKoq//vqLefPmoVarqV27Ni1btmTLli0MHz78icvv2rWLwMBA6tevD8Dw4cNZsWIF58+fx9/fP0d1sLOzIzk5mXXr1tG2bVt+/PFH80iL7dq1Y+fOndSsWZPt27fTtm1blEolTk5OrF+/HqVSydChQ1GpVFnW+yL+Zo9KTExk5MiR9OjRg5IlS1IQhMWn06Omr8W04q622Z5cnGysmNyuPEaTif/beJGE9ILzqMKL1HrQW7Qe9Jb58/y3+xIXEYard2akMe5uGA6u7tg6OD5pFdmKuxvGxYO7aNJziHmaQacrVKOAdhk2gS7DJpg/f/nmq8REhOLhm5naGX0nFCc3D+yesG32rl3C4S2/0XPsRCrVaWSeHh0Ryuk/d9C23zDzNL1OazEiekF2N0lDuwqWdfVxsuFusmVH4F6KBoPRhI+jjTkN3ddJTeT93yOTtJT1sLxQVioUKICoZC0Gowm7Rwb9+Te8yuRiSDT/17+pxbSKJT25FGJ5TlIoFLg52dJ70q/o9Jk3CN/oXIdLIdGka/592Tf/ZQ37jaVhv7Hmz2s+7E9SZBjOXkUASIwKw87FHbV91uOMa5GSJEY9HJRQm55KemI8LkUennNDzxyhSKVa2Dq5WCx7YPH/8CpbmUrNM5+HNpmMGA0G1PYFd5C7NoPfos3gh+erH8f1IfZuGK4+989XEbk7Xz1w7eQhSlX1x97Z9UVUN9/cjk+np79lx7K4qx2hCVkfXXG2tWJK+woYjSbGr79IfHrm8cXLUU21Ik5U8HZgUP0SQGbA4pP2Ffj1VAS//X03y7oKkvp9x1C/7xjz53UTXycxKgwn834V/vT9KjKb/co3c3+7eXQ3/l0Gmecb9TqU90dEv/LnJpx9S1CkUi2L+YXp2kcUHM8dAY+MjCQyMpK6deuaf86dO2fupDk7P0ztedBBfEChUJhTzVxcXHB0fLhz+Pj4EBsby927d9m6dat53Y0aNUKhUBAREQGAp6dlmtFXX31FgwYNGD16NGfOnHne5gCgUqlwd3c3t0+v19O4cWNzHbZv305UVNRT1xEREUGRIkUs2urr6/vM5R7l4+PDrFmzCA4OpmPHjnTp0oULFzJfgdW+fXt2794NwM6dO2nfvj0AP/zwA4mJiQwePJjmzZuzcePGLOt9UX+zB+3s27cvFStWZNIky1d75Kfzd5PR6o30rFUEK6UC/+LO1C7uwp/XYy3KKRXwUbtyJGv0fLTlyr+2852dKo3bcOyPFaQmxJGaEMexP1ZSpXGb516PrYMTZ/Zu4uS2tRiNBuLuhnHsjxVUb/Hk7I6Czr9ZO/atW0pyQhzJCXH8uX4p/s3aZVv26I7fObx1LUOnfm/R+Qawd3Tm2M6NHNy0BqPRQHREKH+uX0a9Vk8fGKiguBqditZoJKiiJyoFVPFxoKqPI8dDEyzKaQ0mTt1Joms1b2ytlHg5qAks58HR++WOhiZQwtWWRqVcUQDVfB2p4GXPqTtJZOiN/B2RROeq3jjaqLCzVtKxihd/RyQV6lTI4NMhZGh0vD+gGdZWKtoElKNt/XKs2mkZdTSZTCz55FXG9myAQqGgRjlf3n+9GXPXHsunmou84le/FWe2rSQ9MY70xDjObluFX/1W2Zdt0JrrR3YRE3IVvVbDyd8X4Vm6Ii4+D8dcib55EW+/qlmW9SxdkfO7fiPpXgQGnZaT63/G3sUd77JZyxZUVZu04ciG5ebz1ZGNK6ja5PnPVw9EXLtIsQrVXmAN88e5iMxrnd7+RbFSKqhTwoW6JVzYdzXGopxSAZ8EVSApQ88Hmy6bO98A0Slauv18gt6/nDL/aPVGpm67WuA739kpW78V57auMu9X57atouwT9quy9Vtx8+huYm5n7lenfl+ER+kKOPsUw8beiavBW7i4ez1Go4HEqHDObV9N+SZBAKQlxnHit3mkxEZh0Os4v2MNOk06xavXz8vmFihKhSLffwqr546Ae3p64ufnx6ZND0csDQ8Px83NjfPnz1u8G+9pkpOT0Wq1qO8PTBQZGYmPjw9eXl707t3bYgTvGzduUKpUKfbv32+x/sWLF5OQkEBwcDA2Njbs27eP6dOnZ/t9CoXCPCibyWQiMTHxie1zdHTkr7/+Mn/Xg+j603h7exMaGmr+bDQauXv3Lh4eOR/IKy4uDnd3d5YtW0ZycjKzZ89mypQp/Pbbb9SpU4e0tDQOHjzIvXv3qFu3LlqtlqioKL7//nt0Oh07duzg/fffp1UrywPPi/qb3bhxg0GDBtG1a1cmTJiQ4+Xygt5oYtqOa7zZuBTdavgSn67ju/23CE/IoIqPI5ODyjN27QWKu9pS0dsRjd7IstdrmZfXGYwMWJ67GziFRYOu/dFpNSz/ZDRGvY6K9VvQ8JH3di+ZOIzKDQMJ6JT1lTiPsnd2pduETwn+dQFHN67A2taW6s07UDfo1ZfdhJcmsOdAdNoM5rw/DINeT41GLS3e2/39O4Oo0aQ1Lbr1Z//vK9BmpDP/o9EW6+gx+gOq1m/OwA9nsG35PPatW4La1o66rV6hcafeed2kXDGYYM6hUPrUKkKbCh4kZehZcuIOkcla/DzsGd24JNN3XSc+Xc/qv+/Sq6YvU9uVw2SCA7fiOXgrAYCIJA0/HAqlazUfetTwITFDz+K/7nAnMTNCvvRkBF2revNhYFlsVEouRKWwuhBe+D1KpzfQ+d3lfD/hFSb0bUxUXApvfPY7l29H07hGSTbM7E/tAXMIu5dI/09+Y9aEjnwwsBmxiWl8v+YIC/8o2G8WEf9crY79OanVsOmLsRgNekrXaYZ/p4eDOm6Y/iZl67WkRlBvilWpQ+0uA9m/aAaalES8y1ah+bAPLdaXHBtFKf8mWb6nUotOaNNT2PHdBxh0Grz9qtJ6zPRCFalr1O119FoNSyePwqDXUalBCxp3H2iev/jDzPNVg85PP189kBgTSYWAps8uWMDpjSY+3nqFUU1L82qtIsSn6fh6303CEjKo6uvI1A4VGbnmHMVdbankk3mts3rQwzcsaPVG+i39d6VM1+jwGnqthi0zxpn3qwfv7QbYNH0kZeq1oFpQb4pWqUOtzgM4uOhLNCmJeJWtQrOhmfuVrZMLgaOncnL9z5zbthIrGzvKNQ6iSqvuANR85TWMeh07vv4/dOlpuJcqT6sx01HbFdzMElFwPXcHvFatWqSmprJu3Tq6devG+fPnGTx4MIsWLXqu9ej1eubMmcOYMWNYv349JpOJWrVq4ebmxoABA+jcuTPVq1fn119/5dtvv+XPP//Mso6UlBSsra1RKpVERUXx3XffodNl3uVTq9Wkp6djMBhQqVSUKlWKzZs3M3ToUH755RdSU1OzrA+gWLFilCtXjjlz5jBixAju3LnDwIEDef/995/6/HZQUBA9e/ake/fu1K5dm59//hkrKytq1qz5xGUeFx4ezvDhw1m1ahWlS5fG0dHRHJ1WKpW0a9eOL7/8knbt2qFUZiYvjBs3js8//5w2bdrg4eGBra0tarUatVptfsb7RfzN0tPTGTFiBL1792bMmDHPXiAfhCdk8NGWK1mmX4xKoe+SzBNOTKqWrgtzdqG791ose6/FPrtgIWFlrablayNp+drIbOcP/HxBttMbdsv6Luyi5avS56PvXmT18pWVtZqOg8bScdDYbOeP+/oX8+/vz1v71HWVrFiNEdN/eJHVy1ORyVq+O3A7y/QbsWlM+OPhoIvpOiNLTkQ8cT3XYtL435+3sp2nM5j47WwUv53NeYZQYXDldgzt3voly/RDZ0PxCvrc/Pns9Uhajiqcgxa+LKF347DzL5jnlhdFZW1NQK83Cej1Zrbzu06eZ/G5QpP2VGjS/onre7z8AwqFgpod+lGzQ7/cVzafWVmrCew/isD+o7KdP/iL7M9XT3q/95PKF0ZhCRl8uCnrALgXIlN4ddFJIDPK3fGn4zleZ7efC+8NQJW1NfV6jqBezxHZzu80+UeLz+WbtKf8E/Yrr7JVCPq/r7Odp1RZUafHUOr0GPrPKiwEuUhBV6vVzJs3zzw42Ntvv82HH374XB1NyEz7Tk1NpXHjxqxZs4Yff/wRtVpN+fLlmTZtGhMnTqROnTqsWbOGefPmYWeXdeCNQYMGkZiYSEBAAH379qVjx44kJCSQmJhI+fLl8fLyon79+mg0GqZMmcLGjRupX78+YWFh1K795Heufvvtt5w/f54mTZrQv39/+vbt+9TON4Cfnx8zZ85k2rRpBAQEcPDgQRYsWGCO8OdEjRo1GD58OAMHDqR27docP36cjz/+2Dy/Q4cOXL161Zx+rlar+fbbb/nuu+/w9/dnypQpfPPNN6jVaurWrUtCQgLt2rV7IX+zvXv3EhYWxs8//2x+F7y/v/8zR3UXQgghhBBC/Lvk9zvAC1Ai7nNTmPLh/R/h4eG0bduWixcv5vVXF2oRERH06dMnSyp+YZHTyPN/TVDVZ78+5b/Mw65wDGCWH/bdTMjvKhRoi79bk99VKLiSY55d5j9s8lfj87sKBZaX43/vdXk5teHvf1dWz4vWwM89v6tQoE1uXS6/q/Bcpu++nt9VKHTb7AE5ihYS165d45dffqFLly6FsvMthBBCCCGE+HcoxG8OzXfSAc+h5ORkmjVr9sT527dvx8fH55nr6dOnD1euZH1OGeCzzz57Yqr7mDFjsLe3Z/HixTmrsBBCCCGEEEKIAiVfOuDFixcvdOnnTk5OnD79z0eOXL16da6W27Fjxz/+biGEEEIIIYQQ+Uci4EIIIYQQQgghckyB5KDn1nOPgi6EEEIIIYQQQojnJxFwIYQQQgghhBA5JoOw5Z5EwIUQQgghhBBCiDwgHXAhhBBCCCGEECIPSAq6EEIIIYQQQogckxT03JMIuBBCCCGEEEIIkQckAi6EEEIIIYQQIscUCgmB55ZEwIUQQgghhBBCiDwgHXAhhBBCCCGEECIPSAq6EEIIIYQQQogck0HYck8i4EIIIYQQQgghRB6QCLgQQgghhBBCiByTMdhyTyLgQgghhBBCCCFEHpAOuBBCCCGEEEIIkQckBV0IIYQQQgghRI4pJQc91yQCLoQQQgghhBBC5AGJgAshhBBCCCGEyDF5DVnuSQRcCCGEEEIIIYTIA9IBF0IIIYQQQggh8oCkoAshhBBCCCGEyDEZgy33JAIuhBBCCCGEEELkAemACyGEEEIIIYQQeUBS0EWeKe/rlN9VKJBC4rX5XYUCbePfUfldhQLLTi2H8Kf5dPqA/K5CgaXRm/K7CgXa9P/7Nr+rUGCpytfN7yoUWN+/G5jfVSjQ+vmXzO8qiBdIieSg55ZEwIUQQgghhBBCiDwg4RMhhBBCCCGEEDkmg7DlnkTAhRBCCCGEEEKIPCAdcCGEEEIIIYQQIg9ICroQQgghhBBCiBxTSgp6rkkEXAghhBBCCCGEyAMSARdCCCGEEEIIkWNKGYUt1yQCLoQQQgghhBBC5AHpgAshhBBCCCGEEHlAUtCFEEIIIYQQQuSYZKDnnkTAhRBCCCGEEEKIPCARcCGEEEIIIYQQOSaDsOWeRMCFEEIIIYQQQog8IB1wIYQQQgghhBAiD0gKuhBCCCGEEEKIHJMM9NyTCLgQQgghhBBCCJEHJAIuhBBCCCGEECLHJIqbe7LthBBCCCGEEEKIPCAdcCGEEEIIIYQQIg9ICroQQgghhBBCiBxTyChsuSYRcCGEEEIIIYQQIg9IBFwIIYQQQgghRI5J/Dv3JAIuhBBCCCGEEELkAemACyGEEEIIIYQQeUBS0IUQQgghhBBC5JhSBmHLNYmACyGEEEIIIYQQeUA64EIIIYQQQgghRB6QFPT/qLi4OOzt7bG1tc3vquRYUmQoZ3+bQ2LELWycXKnScSBFazbOUk6XkcaZ3+YQfeUUCpUVpRu0o2LQa+b3Fd49d4SLW5aSkRCNc9Ey1Ow5GucipQHISI7n3Lp5xNw4j0KpxLtibap3H4G1rT0mo5ErO1YSenw3Ok06Tj4lqNJxAJ7lauTlZngio0HPuQ0LCT8djMlopFitptTsPhyllXW25W8d2saVPb+hTU3Co0wV/HuPxd7NC4D0hFhO//o9sbcuYWVrT4VWr+LX9JUs67i273fuXf2bxiOmmqcl3rnF2Q0LSbxzA5W1DUVrNKRap8Go1DYvp+H/UEk3O8Y1L0NZT3vi03QsPhrGwZtxWcp5O6l5s3FpqhZxxGiCU2GJ/HToNkkZegCmdahIzWLOGEwm8zJjfzvPncSMPGtLXijhasubjUtR2sOOhHQdy/+6w5GQhCzlvBzVvNGgBJV9HDGaTPx9J4lFR8NI1hjyvtIvkFGv5/Ca+dz4az8mkxG/us1o1OdNVE/Yzy7u38Lf29aQkZKIb7mqNBvwNo7umftZbPgtDq+eR0zodWwdnaneuhvVAjublz2zYy0X9m1Ck5aCW5ESNHh1KL7lq+VJO3PLaNBzfO18Qk5kHodK12lKQK8nb58rwVs4t3MNmpQkvP2q0ui1t3Bw9yLq+nl2z/nYct36zH3t9dl/YNBp+WvtAm6fPohep8WzVHnqvToc9+JlX3ob80PZEp4cWPYutXt8SlRscn5X56WrXNyVWcMbUqO0O1EJ6Xyy4iQbjt22KFPcw4ET33a1mGatUmJjraLciF+JSkjno17+vN6yHE52ai6HJ/DJypMEX4jMw5a8HAa9nr0rfuTSkT8xGo1UbtCc1gNGP3E/e+D41t8IOXeKXu9/YZ4WdzecnUtmE3nzKlbWaio1aE7LvsNRWf07ugg6nY7/ffkFO7ZtwWA00i6oPR98+BHWanWWsgaDgQU//cjv69aSmpqCf+26fDDpI4oVK54PNS/4JAE99wpkBDw8PJyKFSvSs2fPLPPmz59PxYoVOXbsGP7+/kRFReVJnWbPns2kSZPy5LvyQvv27UlISMj18vHx8bRo0YLIyLw5kRn1Oo7/PA2fKvVo/+lKanQfwenVs0iLy/r3P7t2LkadljaTF9FkzJeEnw4m/NSfACRHhXFq5TfU6PEm7T9dhXflOvz1ywxMRiMA59b/hLW9E20/+YXA938kPSGGS1uWAhByZDt3zhyiybj/0eGz1ZQMaM3xRZ9h0OvyZBs8y6XtK4kPu07rD36k9ftzSbxzg6t712dbNuryKS5uW0aDNz6i4/QV2Lt7c2rVLPP840u+xN7Dlw6frqD+4Ilc3rGSmJsXzPN16amcWf8T5/9YZLFeo17H4QVT8SpXjQ7Tl9Ni/DfEh17lwtZlL6fR/5CVUsHUDhU4djuenotOMvdACBMCy+LtlPXEPKlteRLSdQxY9jfDVp7BQa1iXPMy5vl+nvZ8uOkS3ReeMP/82zrfVkoFE9uW40RYAgOW/c3Cw2GMaVYaL8es2+vdwLIkpOsYtvosY9ZewEGtYmSTUvlQ6xfrxKblxIReo9e0n+g15SdiQq9zZsfabMuGXzjJXxuW0nb0xwz45lccPXzY/8s3AGjTU9n2/WQ8SpTh9a9XETRmCmd2rOVS8FYAQs8e5/zejXT6v5kM+u43/AJasP2HKeZOaEH19+blxIVep+vHP9Hl43nEht3g/K7st8+di6c4vWkZgSM+pvfM1Th6+HBo2bcA+JSrxmvfrjf/9Ji+GDsXNxq+Ng6A05uWER9xiy4fz6Pv12vw9qvC/oWf51k789KrbWuzZ9EE3F0c8rsqecJapeS3D1qx/WQ4xQev5J2fjzFvdBNKejlalAuPTcV3wArzT6k3VnMxLJ7P1pwmMj6doW0q0q1haQInbaXIwBUs3XuN1e8GorYqkJe+z+XguiVE3rrG0Jk/M/TLhUSFXOfY5jVPLK9JS2X30jnsWzk/y7xNc7+geIVqjJu3loGfziHk3ElObM/+2qEw+nHObC5dvMDvm7bx+x9buHTxIosXLcy27PKlv7B2zWq+mz2HfcGHKVe+PMPfGIRGo8njWot/uwJ7FFIqlYSEhBAREWExffv27djb2wNw+vRpfHx88qN6hd4/6XyfO3eO1157jbt37764Cj1DzPVz6DPSKdeyO0qVFd6V6uBdqTa3j+60KKfXZBBx5hAVg/phZWOHo1dR/Jp1JuTwNgDCTuzFq6I/XuVrorSypkLr3ugyUom+dgaAlHt3MBkNYMrskCsUmCO3pRu1p/n4b7F380KXlowuLRlreycUioKxG4Ue30PF1j2xcXTB1sWdim37cOvw1ieWLVE3ENdiZVGpbajWaTAxN86REh1B8r1w4m5foUrH11FZWeNeqgIl67Xi1qFt5uUPzv0ITXICZRq3t1hvekIsLkVKUbFNL5QqK+xcPShRpyVxty691LbnVo1izthZq1h7+i4Go4mTYYmcDE2kfWVvi3K2VkoSM/Qs+yscjd5IitbAtov3qOLrBICHgzUudtbciEnLj2bkmWpFnLCzVrHhbBQGE5y+k8Tf4Um0qehpUc7WSklShp5fT0WgNZhI1RrYeTmGSj6OT1hz4XH18C5qBfXGzskVe1d3ar/Sj4v7t2Rb9srhXVRo2ArPEn5YqW1o0OMNIq6eJTHqDpHXL6LLSKfBq8OwslbjVrQUVVu+wqUD2wFIiAzDZDJlHo8AhUKBVQHNInnU9aO7qdauF7ZOLti7uFOzQ1+uBGd/HLp+dBd+9QNxv7996nQbQuS1syTdu5Ol7JGVs/EpV51yDVoDULvrINqM/RRbRxcykhPRpKVg6+T6MpuWL0b1bc7kkR2ZOndTflclzzSr5ouTrTXf/nEOvcHE7jN32P33HQa3Kv/U5T7p409qhp4v1maezxfsvEKT9zcRHpuKu6MN7k42xKdoMRhNT11PYXD+wE4adOqDvbMrjm4eNOrWn9N7nvw/svqLd0lNjMe/Vacs8+LuhmE06DE9sl2sbQr+sSan/tiwnjeGDsfd3R0vL29GjBzNml9XZVt2547t9B8wiMpVqmKtVjP2rfHExsRy9MjhPK514aBQ5P9PYVUweg7ZUCgUtGzZkp07H3awQkNDUalUeHpmXuxVrFiRyMhIjh07Rp8+fZg4cSK1a9emQ4cOHD9+3LxccHAwnTp1IiAggFGjRhEbGwtkRrVHjx5NYGAgffv2BeCnn34iKCgIf3//LOvJicDAQObMmUOjRo1o0qQJa9Y8vCO5b98+unfvTt26dWnSpAnLly8H4NixY3Tv3p2+ffvSsGFDYmJinlq2b9++TJo0CX9/f7p168bp06fp168f/v7+jBs3DuP9aG5ISAiDBg2iXr169OjRg7NnzwLw2muvARAUFMTFixfR6XR8+eWXNG3alObNmzN//nyL9kyePJmAgABWrFhBaGgow4YNY8SIEc+1Xf6p5HthOHoXR6F8+C/r6F2cpLuWKWmpMRGYjAacfEpYlEu+Xy4lKhwnn5LmeQqFAkfPoiRHZs4vH9iDiDMH2fJhb7ZP7odBr6NS0GvmslY2tkScOcT2T17nyo5VVO86DKVK9dLanVPa9BQykuIs2u3kXZyMxDi0qVnTFZOjwnD2fVjW2s4BGyc3Eu+GkBwVhq2zG2q7h50lJx/Lbd3gjY8IGPg+No6uFut18PSl0YipKJQPt0nkheO4lvB7Ec184Uq62RGekMGjl2NhCemU9rC3KJehN/LxlivEpz3Mdqhf2o3r0akAlPN0IE1rYHK78qweVJvZr1ajfinXPGhB3iruasudx7ZXeGIGpdzsLMpl6I18uvM68ekPo7X1SrkU+hsUmrQU0hLjcCv68Bji6luCtIRYMrLZzxLuhuFW5GFZtb0D9s5uxN0JAZMJlbW1xfFDoVSSGBUOgF+95qjt7Fn90RssHNWJv35fQusRE1EW4LRQbVoK6YlxuPo+bLOLTwnSE2PRZLN9EiPDcHl0+9g5YOfsRvydEItydy6eIvLKWQJ6PjzvKJUqrNS2nN+1jt8mvs7N4/uo22PYi29UPlu/6zS1un/K3qNX8rsqeaZiMVeuRCTyyNM8XL2TSJWSbk9cplIxF4a3q8yYnyw7SmkaPV3rlyJkYR8m9qzFu4uPFfoOeEZqCikJcXgWe5hR5FGkBCnxsaSnJGW7TPfx0+gy9iPsnV2zzGvU9TWObV7D10M6MndsXzyKlcS/deesKymEkpKSiI6OpqxfOfO0MmXKEn3vHonZBKJMRiN2dg/PZwqFAqVSwe2QkDyorfgvKbAdcMhMk96xY4f589atW+nQoUO2ZU+fPk3VqlU5duwYr7zyClOmTAEgLCyMCRMmMHnyZA4ePIifnx/vvfeeebm//vqLlStXsmDBAo4dO8aaNWtYtmwZJ0+epG3btnz99dfPXe/9+/ezdetWfvzxR7744gsuXLhAamoq7777LpMnT+bEiRPMmDGDGTNmkJKSAsCFCxcYMWIEu3btws7O7qllT506Rb169Thx4gTu7u4MHz6cqVOnsmvXLk6dOsWRI0fQ6/WMHDmSZs2acfjwYUaOHMnIkSNJSUlhxYoVQGY2QZUqVViwYAFnzpxh48aNrFmzhk2bNrFly8OITlpaGgcPHqRr1654enqye/duunTp8tzb5Z8waDKyPEOsslZj0FmmBek1GShUVihVVo+UszGX02szUFlbpsuq1DYYtJnzTSYTpRu2p/2nK2kzeREKFJz73TJly6dqAK98uY7qPd7kxLKZJN+/YM5PBk1mqvOj2+jB749vI3iwHR7bnve3gz7bbW1jsR47V49n1slkNHJm3TxSoiOo1K5vzhuTh2ytlGj0RotpGr0Rm2ekKL5aqwhNyroz/3DmTQlrlZLL91JYeCSU15aeZs3pCCa2LU95r39XyqittRKNwXJ7afXGZ6Z0dq3uQ4PSbvxyLOxlVu+l02nSASwi0Q9+12uzPm6g06RniVpbqW3RazX4lquKQqHk1OaVGHRa4u+Gcil4GwZd5k0eg16HRwk/ekyew+DZv1OrQ292zfuMjCdcYBcEuvvHoey3TzbHoWy3j02Wsme2rqBK627YODhlWUflFp3o/90GqgR2ZfcPk7Pt6BdmkTFJmEyFu8P4vBxtrUjXWD5qkabVY69+8s2nd7vXYPWBG1y/m3X/2HoyDPfXljJ+4VGWjm9BhaIuL7rKeUqbcf849EiU+sHvuiekSju5e2Y7PZOCJt1fZ/zCP3jjy4XEhN/myB8rX1h981N6WuZNX1u7h+MdPfg9IyPrMbtVm3YsX7qEmzduoNVq+XHObDIyMtBmc/wS4p8ouLfSgcaNG/P+++8TFRWFj48PO3bsYO7cuaxcmfXA4OjoaI7sduzYkblz5wKwZcsWWrVqRUBAAABjx46lTp06xMTEAFCrVi18fX0BqFGjBitXrsTT05PIyEjs7e2Jjo5+7nqPGjUKV1dXXF1dCQoKYufOnYwbN44NGzZQvHhx4uPjUSqV6PV6EhMTAbC3t6dFixZA5iAQTyvr4uJC165dAfD398fW1pby5TNTsypUqMDdu3c5e/YsGo2GIUOGANC6dWuWLFlCcHBwlpsYGzduZOrUqbi7uwMwZMgQfv/9dzp27GheVq1Wo85mwIq8olLbYtBpLaYZdFqs1JaDyFmpbTAZ9BgNBnNkyaDToFLbmecbH1+PVoPKxpaMpDj+XjObdlOWYm1rj7WtPVVeGcShuR9Svdtwc8f9wSAnpeq3JeTwNu5dPoGTT94P0HF6zRzCTv55/1PmBdqj2+jBTQWVTdaB9lRqm6zbU6vBysYWTGTdRjpNlm39NNq0FE4s+x/pibE0HTsDW6cnRy7yU4beiNrKMofJxkpJui77gcKsVQrGNitDreLOvLfxIuEJmSfwgzfjLAZuO3AjjlYVPGlYxo1r96Pk/wYZOiNqleX2UlspyXjsJsYD1ioFIxqVpEZRZz7ecpU7iYXvIubA8tlcO7Y388P9jpDhkYuxB51Faxu7LMta2diif2xf0mszsLaxRW3vQPtx0zjy60+c27MB96KlqNCgFRf2ZaaRHl49j2KVa+FRInNQMf/2vbl6aBe3Th+ictP2Wb4rvxxZOZubf+3L/HB/+zza5ofbJ+vxw0pti0H7+PbRWJRNuBtK7O1rBI74+PHFAczH5Zod+nF5/yYir56llH/WwTlF4ZGq0WP3WGfbXm1Fiib78Q+c7azp2qA0zSduzna+9v7xaem+awxrV5G2/sW4GpH4Yiv9ku1Y9B0XDu2xmKZ/ZN/R3+94q22zHoee5u7NKxzdvJpxc9eiUCrxLFaKxt36s2/VfBp1ee2fVzwfTJ/6MVs2ZR5HTfevjTQZD4/ZGemZ5207e/ssyw4cPIT09DRGDn8DEya6dX8VP79yODs750HNCx9FYc4Bz2cFugOuVqtp0aIFu3fvplGjRtja2lKkSJFsyz7oPAKoVCpzGvbdu3fZunUre/Y8PHApFArzs+UP0tkf+OqrrwgODqZYsWJP/K5nKVasmPl3Hx8fYmNjUalUbN26laVLl+Lo6EitWrUAzHe2PTweRhSfVfbRA4FSqcTJ6WFUQKFQYDKZiIyMJDIykrp165rn6fV6cyf/UXfv3mXUqFEo76d3G41Gihd/2KH08vLK1XZ4kZx8S3Jt71pMJpN5h0+5F47TI2nUAA5exVAoVaRE38H5fhpkyr1wc2q2k29Jku89jFibTCZSou/g5FMSTVI8JoMek+HhSV6hsgKFEoVSycXNS0ABVToONM836nVY2+ZPlNO/12j8e402f972yUBSosJx9Mz8v02+F46ts7tFKvkDzo9tB116KhlJ8eb0/PTEOHQZaVjbZp6gkqOybusnSY2J5NBPH+PkW5Jm42aa11EQhcal07t2UYtpJVztCI1Pz1LW2daKaR0qYjCZeGvtBeLTH6ajtyjvgd5gsuiEW6uU5gu/f4uwhHR6uPhaTCvuYkt4NtvLyUbFR+3KYzCaeO+PSySkF+zBw56kaf+xNO0/1vx5+buvkRAZjrN35v9NQmQY9i7u2Nhn3c/cipYiIfJh1F+blkpaYjyuRUti0GkxGg10evd/5vnH1y/Gs1RmqmRKfDRGg+WNIKVKhUpVsE7bDfuNpWG/h9tnzYf9SYoMw9kr8ziUGBWGnYs76my2j2uRkuaUe8gcmC49Md4iLT30zBGKVKqFrZNl1PLA4v/hVbYylZpnvp3BZDJiNBhQ2/+7sk7+iy6FJfB/XatbTKtQzIXL4QnZlm9Xuzi3opI5fzveYvrUfnUwmUxMWXXKPM3GSkVimvbxVRR47Ya8Tbshb5s/zxnTm7iIMNx8Mo9DsXfDcHR1x9bh+cbZSI6Nxqg3YMJkHtFaqVI9czT1gmzyJ9OY/Mk08+fWLZpw69ZNSpTMPK7cunUTLy+vbDvVUVGR9OzVhzHj3gYyU9h/WbSQylUK9tsnROFToFPQIfM55R07drBt27Ynpp8/jZeXF7179+bEiRPmn99//50qVaoAlndvFi9eTEJCAsHBwaxfv55XX301V3V+EF0HzNH7EydOsHz5ctatW8f27duZPHmyRVrZo/V4nrJP4unpiZ+fn0W7N2/eTJ8+fbItu2zZMnO5ffv2sWDBgly1/WXx9KuGylrNtd1rMOp13Lt8knuXT1K8dguLclY2thSt0YhLW5agS08lJTqCG8F/UKJeIADFarcwL2vU67i6+1dUals8/arh5FsSew9fzm/8Gb1WgyYliUtbl1K0RmOUKivcy1Qm5PA2EsKuYTQYuLF/I5qURHyq1s+HLZJVyXqBXN65moykeDKS4rmy81dK3m931rKtCD2+m/jQqxi0Gs5vWox7qQo4eRfDybsYbiXLc/6PxRi0GuJDrxL61x5K1mv1zDroMtI4NG8ynuWq02DIpALd+QY4G5GERm+kT+2iWCkV1CnhQt2SLuy7GmtRTqmAqR0qkpSh54ONlyw63wB2VkpGNi1FaXc7lAoIrOBJZV9H9l6L4d/kwt0UtAYjr9byxUqpwL+YM/7Fndl/w/K1bUoFTGpbjqQMPR9vvVpoO9/ZqdCwNSc3ryQtMY60xDhObV5FhYatn1j2yqFd3Au5gl6r4ei6n/EuUxFXn+KYTCa2fDMx83VmRiMRV85yMXgr1QIzH+8pVaM+5/ZsID7iNkaDgQv7NpGWFE/xanWz/a6Cwq9+K85sW0l6YhzpiXGc3bYKv/rZHzv8GrTm+pFdxIRcRa/VcPL3RXiWrojLIxlF0Tcv4u1XNcuynqUrcn7XbyTdi8Cg03Jy/c/Yu7jjXTZrWVG4HLgQSbrWwHvda2CtUtK6ZjHa1CrGr8E3si3foKI3Ry5nfSPK0StRDGtXCf+yHqiUCkZ3rIKnsy1bTxTuR2EAqjVty6ENy0lJiCMlIY7DG1ZQrWnb515PsQpVUapU7P91EQa9nsSYKI5sXEmVhi1fQq3zR6cu3fjpxznEREcTEx3N/Hlz6dSlW7Zlt23ZzDvjx5GUlERycjJffvEpFSpWolr16tmW/69TFoCffyo6OpohQ4aYx/46efJktuWMRiNffvklTZo0oX79+kyYMMGcmazT6ahWrRr+/v7mn6VLlz71ewvWrfRsNGnShA8//JCoqCiWLXv+Vxm1a9eOAQMG0LlzZ6pXr86vv/7Kt99+y59//pmlbEpKCtbW1iiVSqKiovjuu+/Q6Z7/FVPz58+nevXq3Lp1i127drFq1SoiIiJQKpWo1WrS0tKYPn06kBmVflxqamqOyz5JrVq1SE1NZd26dXTr1o3z588zePBgFi1aRM2aNbG2tjY/U96xY0dmz57NzJkzUSgUjB8/npIlS5qfoy8IlFbWNBg2hbPrfuT6vvXYOLnh33c8Tr4lib15gaMLptLyvTnYu3lR49WRnPt9Pnu+GAEKBaUbtad0wyAgM/Jb+7V3uPDHItISYnApUpr6Qz82vyu7wbBPuLDxZ3ZPH4LCypoi1eqbI96+VQOo8sogTiz7H7q0ZFyK+dHozU+xcSwYqUmVgvph0GrY+9VbGA16ivs3pXL7hylku2eMokSdFlRs0wufSrWp0mEAfy39H5qURDzKVCZg4AfmsvUHfcjfv81h25SBWNnYUaX96/hUqv3MOoSfCiY1NpKM5HjCTwWbpzt5F6PlO9+90Pa+CHqjicmbLzOmWRl6+hclLk3L//bcIDQ+napFnJjesSIjVp+lhKsdlXwc0eiN/Dqkjnl5rd5In19Ose1SNE621kzpUBEXWytC49P5ZOsV7iUXvkjL0+iNJqZtv86IxiXpWt2XhHQds/aHEJ6QQWUfRz5qV4631l2gmKstFbwzt9eS/jXNy+sMJgatOJOPLfjn6nR6Db1Ww7rpYzAa9PjVa07dzq+b5//2yQjK1W+Jf4c+lKhah4BuA9kzf0bme8D9qtJ6xEQg83GYtqMmc+TXn9i/9Dsc3b1o0ncUJe53sOu88homk4lt33+MNj0V9+Jl6PD2Z9g7F8zHOR6o1bE/J7UaNn0xFqNBT+k6zfDv9HD7bJj+JmXrtaRGUG+KValD7S4D2b9oBpqURLzLVqH5sA8t1pccG0Up/yZZvqdSi05o01PY8d0HGHQavP2q0nrMdFTWhTdyJzLpDEa6fb6L74Y25O3O1YhKSGfYDwe4fCeRRpW8WT+xDXXHbyA8NvPxnlLejpy6EZtlPdtOhvPRshMsebs5bo42nLkVyyvTdxCbXPgehXlc4+6vo9Nk8MukkRgMOio3aEGTHg+z8xa+P5SqjQJp2KXfU9fj4OJGr/e+YN+q+cwe1RO1nR3VmrShcbfXn7pcYTJy9FgyMtLp/Wo3dDodQe07MGrMOPP8bp070vGVTgwd/iYDBw0h4s4dOrVvg9FoolHjJnw/50dJtf4Xmzx5MuXKlWPevHls376d8ePHs3fvXqweG/B09erVHD16lN9//x17e3vee+89vv76a6ZNm8aNGzfw9fVl9+7dOf5ehakAju4RHh5O27ZtuXjxIgDvvvsukZGR5g54mzZt+PTTTxkwYAD79+/n9u3bfPTRR+zatSvb5Xft2sWsWbOIiIigdOnSTJ48GX9/f2bPnk1kZCSfffYZAPfu3WP8+PFcvHgRNzc3+vTpw+zZszl48CBLly61KPskgYGBNGnShD///BMrKyv+7//+jw4dOmA0Gpk0aRI7duzA3t6eNm3acPLkScaPH4+9vb1F/Z+n7Ny5cwkNDWXGjBkAvPHGGwQFBdGzZ0+uXr3K9OnTuXTpEs7OzowaNcoc1Z84cSJbt25l3rx51KpVi6+++oodO3ag0Who3rw5U6dOxd7ensDAQGbOnGmRyv5AxYoV2b9/v/kZ+md5d/N/ZxTX56FSyoH9ac48llYoHnr8OUlhqXG5gt1ZzU8afYE79Rco0//v2/yuQoGlKl+wszDy0/fvZp91JjL18y/57EL/YbaF7JT+6+msr4zMa739iz270BOkpKQQEBDAkSNHcHHJfMypW7duvP322zRv3tyi7MKFCylRogTt2rUDMt9sNWvWLDZs2MDGjRvZtWsXP/zwQ46/u0B2wAuzp3VY/+ukA5496YA/nXTAn0w64E8nHfAnkw7400kH/MmkA/5k0gF/OumAP11h64Cv+Tsiv6tAr1pFn13oCS5evMiwYcM4dOiQedr48eOpUaMGgwcPfuqy06ZNIzU1lS+//JKZM2dy9OhR0tLSSE1N5ZVXXmHChAlYPyUjq5D9qYUQQgghhBBC/NfNnj07S+R5zJgxjB37cHDQ3bt3M3r06McXpWHDhtjaWr6hw9bWlvT0rAPLPmr9+vVs3ryZtWvXAmBnZ0fdunUZPXo0aWlpjBkzhgULFjBq1KgnrkM64M9pwoQJ7Nu3L9t5T9vQQgghhBBCCPFvUBDyN8eOHWvR2c5Oq1atuHDhQpbpV65cYcSIERbTMjIysM/mFXUPLFiwgJ9//pmFCxdS8v7I+o9+v4uLC8OHD5cO+Iv2zTffPHX+sGHD8qgmQgghhBBCCCGeRKFQZBlUDaBUqVIkJCSQkpKCo2PmK/xCQkLo1i37UfK/+OILdu3axYoVK/Dz8zNPnz9/Pq1atTJP0+l0qNXqp9apwL+GTAghhBBCCCGEeFEcHR1p3Lgxs2bNQqvVsmXLFqKjo6lfP+vrhVetWsX27dtZtWqVRecb4MaNG3z99dekpqYSERHBTz/9ROfOnZ/63dIBF0IIIYQQQgiRYwqFIt9//qnPPvuM27dv07BhQ+bNm8fs2bOxsbEBYOjQocybNw+AJUuWEBsbS1BQkPld3w862RMnTsTW1paWLVvSo0cPWrVqRe/evZ/6vZKCLoQQQgghhBDiP8XT05P58+dnO2/hwoXm37dv3/7Edbi4uDzzEeXHSQdcCCGEEEIIIUSOSRp17sm2E0IIIYQQQggh8oB0wIUQQgghhBBCiDwgKehCCCGEEEIIIXLsRQyC9l8lEXAhhBBCCCGEECIPSAdcCCGEEEIIIYTIA5KCLoQQQgghhBAixyQBPfckAi6EEEIIIYQQQuQBiYALIYQQQgghhMgxGYMt9yQCLoQQQgghhBBC5AHpgAshhBBCCCGEEHlAUtCFEEIIIYQQQuSYUoZhyzWJgAshhBBCCCGEEHlAIuBCCCGEEEIIIXJMBmHLPYmACyGEEEIIIYQQeUA64EIIIYQQQgghRB6QFHQhhBBCCCGEEDmmkEHYck0i4EIIIYQQQgghRB6QCLgQQgghhBBCiByTQdhyTyLgQgghhBBCCCFEHpAIuMgzGr0pv6tQINla53cNCrawO0n5XYUCq1413/yuQoEWm6rP7yoUWGU8bPK7CgWaqnzd/K5CgWW4diK/q1BgBZXvl99VKNDiUrT5XYUCrairOr+rIPKIdMCFEEIIIYQQQuSYUgZhyzVJQRdCCCGEEEIIIfKARMCFEEIIIYQQQuSYDMKWexIBF0IIIYQQQggh8oB0wIUQQgghhBBCiDwgKehCCCGEEEIIIXJMUtBzTyLgQgghhBBCCCFEHpAIuBBCCCGEEEKIHFPIa8hyTSLgQgghhBBCCCFEHpAOuBBCCCGEEEIIkQckBV0IIYQQQgghRI4pJQM91yQCLoQQQgghhBBC5AHpgAshhBBCCCGEEHlAUtCFEEIIIYQQQuSYjIKeexIBF0IIIYQQQggh8oBEwIUQQgghhBBC5JhCAuC5JhFwIYQQQgghhBAiD0gHXAghhBBCCCGEyAOSgi6EEEIIIYQQIsdkELbckwi4EEIIIYQQQgiRByQCLoQQQgghhBAix5QSAM81iYALIYQQQgghhBB5QDrgQgghhBBCCCFEHpAUdCGEEEIIIYQQOSaDsOWeRMCFEEIIIYQQQog8IBFwIYQQQgghhBA5ppAAeK5JBFwIIYQQQgghhMgDEgEX/xq+Tmp61/KluIstSRl6Nl2M5u+I5CzlbK2U9K7lS2VvBwwmE4dCEth6KSZLOS8Ha95tUZr5x+5wPSYtL5rwwiRFhnLmtzkk3rmFrZMrVV4ZSNGajbOU02WkcWbNHO5dOYVCZUXphu2oFPQaivu3NSPOHuHilqVkJETjXLQMtXqOxrloaQDS4qI49/sCYm9dQqFU4l2hFtW6DsPG0Tkvm/qP+Xk78EmXylTydSImRcu3O6+x68K9py7zSZfKlPa0Z/DPJ83T5r7uT/2ybhiMJvO0nnOPcTu2cP3vPK6osw0D6xWjpKstiRl61p6J5ER40hPLq1UK3mtZlp1XYzgemmgxr05xZ7pX92HStmsvu9ovjdGg58yGhYSfDsZkNFK8VlNqdR+O0so62/I3D23jyp7f0KQm4VGmCnV6j8XezcuijCYlkb3fvkPA6/+HR+lKABh0Ws5uXMidM4cx6LS4lShPja5DcS1W5qW38UUx6PXsWzmPK0f/xGg0Uql+cwJfH4XqCdvqgb+2reX2+VO8+u7nAIRfOce6ryZlWTfAhMVbX07lX7LKxV2ZNbwhNUq7E5WQzicrTrLh2G2LMsU9HDjxbVeLadYqJTbWKsqN+JWohHQ+6uXP6y3L4WSn5nJ4Ap+sPEnwhcg8bEn+K1vCkwPL3qV2j0+Jis16zv830ut1zPl2Jnt3bcdoNNCydRBj3/kQa+us+9b7b7/J2b9PWUzLSE9n6Mi3eG3QUK5fvcLc72Zy7eolbGxsadqiFW+OfQcbW9u8as4LJdtGFAYSAf+PiouLIyMjI7+r8cKoFDCiQXEuRKbw/parrD0bxWu1i+Bun/WA26umD9YqBR/vuM53wbepW9yZusWds6xvQN2iqK0K3y5i1Os4tnAaPlXq0eGzlVTvPoJTq2aRFheVpeyZ3+Zi0Gtp+/Eimo79kvBTwYSf/BOA5KgwTq38hpqvvkmHz1bhU7kOx5fMwGQ0AvDXki+xcXKl7ceLaPXBj+gyUjnz2w952dR/zEqlYM7r/uy/EkOjz/7ks82X+ax7VYq6Pvnk2rqKN93rFMsyvXJRJ4YuPknA9H3mn8Le+VYpFbzdrDRnIpIZs/4iy09G8Eb94ng4ZN+BKuVmy4et/PDztLeYbqVU0LmqN282LFHoU9Yubl9JQth12n7wI23en0vCnRtc2bs+27JRl09xYdsyGr7xEZ2mr8DB3ZsTq2ZZlrlymn2z3s2yf17ctoLEiBDavD+Hzp+vwqNMZY4tmfHS2vUyHP59KVG3rjF4xkIGz1hA1O3rHN+y5onlNWmp7Fk2h/2r5ltML16xOm8t+MP8M+ybZTi4utN2yNsvuQUvh7VKyW8ftGL7yXCKD17JOz8fY97oJpT0crQoFx6biu+AFeafUm+s5mJYPJ+tOU1kfDpD21SkW8PSBE7aSpGBK1i69xqr3w0slOet3Hq1bW32LJqAu4tDflclTy2eP5erly+y5NeN/LJ6I9euXGL1skXZlv3yu3ls+/O4+WfAG29Stlx5uvXsi06n48MJo6hZuy6/b9/Pj4tXcfnieRbO+z6PW/TiyLbJO4oC8FNYFcijdHh4OBUrVqRnz55Z5s2fP5+KFSty7Ngx/P39iYrK2ql4GWbPns2kSZOeXbCQaN++PQkJCc+9XFpaGh9++CENGzakYcOGTJkyBa1W++Ir+JzKezlgY6Vkz7U4jCa4dC+VS1EpNCzlYlFOrVLgX8yZrZdi0BpMRKfq2Hc9jiZlXC3Kda7qzfWYNItoZmERc/0cek065Vt2R6mywqdyHbwr1Sbk6E6LcnpNBhFnDlEpqB9WNnY4ehXFr1lnQg5vAyDsr714V/THq3xNlFbWVGjTG116KtHXzqDXpKN2cM5cVm2D2t6R0g2DiLt1KT+anGsBZdxxUKtYdCAEvdHEoWuxHLwWy6t1s3awAYq42vJOUHnWHA+3mO7tZIObvTWX7v67oi+VvR2wtVay7VI0BhOcj0zhXGQKzcu6Zylb0tWWd1qU4XBIPDGplseENxuWoIKXPVsvZ800KWxuH99DxdY9sXF0wc7Fncpt+3DzcPZR2JDjeyhZNxDXYmVRqW2o3mkwMTfOkRwdAUDE+WOcXDWLqu1fy7JstVcG0OTNadg4upCRnIg2PQUbR5cs5Qqy8wd2Ur9TH+ydXXF09aBhl9c4s3fzE8uvmfEeaYkJ1Ax85anr3bV4FiUq1aBa07Yvusp5olk1X5xsrfn2j3PoDSZ2n7nD7r/vMLhV+acu90kff1Iz9Hyx9gwAC3Zeocn7mwiPTcXd0QZ3JxviU7SF8ryVG6P6NmfyyI5Mnbspv6uS53Zs2Ui/gUNxdXPHw9OLAW+8yR/rn3xz64FrVy6x9Od5TJ7+P+wdHIi+F0nZcuXpP3g4VlbWeHn70Kb9K5w/+/fLb8RLIttGFAYFsgMOoFQqCQkJISIiwmL69u3bsbfPjK6cPn0aHx+f/KheoZebzjfAnDlziI6OZufOnWzZsoVLly6xePHiF1u5XPB1UhOVouXRy46oFC1FnW0synk5qlEpFUQma55YrrK3A+U87dl8MfplV/ulSI4Kw9G7OArlw93bybs4yXct0xtTYyIwGQ04+ZQwT3P0KU7S/XLJ98Jx8ilpnqdQKHD0KkrS3dtY2djRcPgUbJ0fdsQizx/HpXi5l9Wsl6KslwO3YlIxPfKPcysmlfI+TlnKKhXwZc9qfLfzOpFJltkjlYs6kaoxMKtfTYI/bM6aUfVpXtHzZVf/pSvibMPdJI3FfhWZpKF4NhkC0ala/u+Py+y6Gptl3vJTEXz1Zwj3UjRZ5hUm2vQUMpLicH5kn3HyLk5GYhza1Kw3X5KjwnD2fVjW2s4BWyc3ku6GAOBRuhJBHy2gRO3mWZZVKFVYqW25um89W6cOIuzEn9ToMvTFN+olyUhNITUhDo9iD48h7kVLkBIfS3pK9o8wdH17Kp3GTMLe2fWJ6w05d4LQi3/T8rU3X3SV80zFYq5ciUi0OO5cvZNIlZJuT1ymUjEXhrerzJifDltMT9Po6Vq/FCEL+zCxZy3eXXzsP9MBX7/rNLW6f8reo1fyuyp5KiU5idiYaEqX8TNPK1mqDDHR90hKTHzKkjDrf5/Rq99ASpfNXLZosRJ8+d08VCqVucyRA/upUKnKy6n8SybbJm8pFYp8/ymsCmwHXKFQ0LJlS3bufBi1Cw0NRaVS4emZeWFbsWJFIiMjOXbsGH369GHixInUrl2bDh06cPz4cfNywcHBdOrUiYCAAEaNGkVsbOYF4uzZsxk9ejSBgYH07dsXgJ9++omgoCD8/f2zrCcnAgMDmTNnDo0aNaJJkyasWfPwrtu+ffvo3r07devWpUmTJixfvhyAY8eO0b17d/r27UvDhg2JiYl5atm+ffsyadIk/P396datG6dPn6Zfv374+/szbtw4jPdThENCQhg0aBD16tWjR48enD17FoDXXsuMtgQFBXHx4kV0Oh1ffvklTZs2pXnz5syfP9+iPZMnTyYgIIAVK1ZgMBgYOXIkTk5OuLu706lTJ/N685NapURnsLzo0BpMqFWW/+I2KiV6o4lHr090BhPW98s52ajoVcuXpSciMBTSaxi9NgOVteWNB5VajV5r2fnRazJQqKxQqh4OBWFlbYNBpzHPV6nVlut5ZP6jru1dR8TZw1Tr8saLakaesLdRkaEzWkzL0BmwVWc9NI4K9ON2bBo7zmfNurGxUnI2PJGvt18j8Mtgfg4O4Zs+NahatHA9D/84WyslWoPl9tEYjFn2K4B0nRHtE3aahHT9S6lfXtNrMm+8qNQP968Hv+uz2S+y3xdtMNzfF20cXZ747PgDfk1eoevMdZRr3oWD8z/JtqNfEOk06QBYqx/erHnw++PHogec3J990+rwhuXUbd8Du0I21sSjHG2tSNdY7hNpWj326icPy/Nu9xqsPnCD63ez3rzYejIM99eWMn7hUZaOb0GFooUrUyK3ImOSMJkK6Yn6H0hPy3y06dHnkB/8rslIf+JyJ44dJux2CL36Dcx2vtFoZNb/PiMs7DYD3yicN7hk24jCokAPwta+fXvmz5/PoEGDANi6dSsdOnRg5cqVWcqePn2aTp06MXXqVBYsWMCUKVPYunUrYWFhTJgwgblz51KrVi1mz57Ne++9x88//wzAX3/9xR9//IGjoyPHjh1jzZo1rF69Gg8PD77//nu+/vprfv311+eq9/79+83fPWDAAKpWrUrp0qV59913WbBgAf7+/hw8eJA333yTrl27AnDhwgV++ukn6tati0KheGrZU6dO0bt3b6ZNm8bw4cMZPnw4K1euxM3Nja5du3LkyBHq16/PyJEj6dmzJwsWLGD//v2MHDmSHTt2sGLFCipWrMj27dvx9fVl7ty5nDlzho0bN6LT6Rg6dCjFihWjY8eOQGba+cGDB9HpdDg4WD5ndeDAAapWrfpc2+dl0BqMWCst74SpVQo0j3UetAYjVkoFSgXmTrj1I+Ver1OU3VdjiUrJ/7T63LJS22LQWdbfoNViZWMZtVSpbTAZ9BgNBpT37/DqdRpUarv767HJuh6dBqtHLqgNOi1n1s4l+tpZGo/+DCef4i+jSS9NutaAzWPPS9paq0jTGCym1S3tRrtqPvT68Vi269l54R47Hxm4bcf5KDrXKkJgFS8uRDx5wLKCTqPP2tm2USnR6A1PWOLf59SaOYTeHxeB+7kAj+4XDzrTj+9f8IR9SKtBlU3ZJ1FZZ94Eq9yuDzcObib6xjmK1Wj0HC3IO7sWz+Li4T0A5o7Ro51tnTbzBoa1jV2u1h97J5TIm1fp+taUf1bRfJaq0WP3WGfbXm1Fiib7G1XOdtZ0bVCa5hOzT9/X6jPPX0v3XWNYu4q09S/G1YinR/tE4fLNjGns2n7/73//2kWjebhvae6P6WNnb//4omZb/lhP2w6dcHB0zDIvOSmRTz9+n+h7UXz/0xLcPQpPBpdsG1EYFdgIOEDjxo25ceOG+TnvHTt2EBQUlG1ZR0dHXnvtNaytrenYsSOhoaEAbNmyhVatWhEQEIBarWbs2LEcP36cmJjMZxFr1aqFr68vjo6O1KhRg5UrV+Lp6UlUVBT29vZERz9/GvKoUaNwdXWlevXqBAUFsXPnTmxtbdmwYQP+/v7Ex8ejVCrR6/Uk3k+Jsbe3p0WLFjg6Oj6zrIuLC127dkWlUuHv709AQADly5fH09OTChUqcPfuXc6ePYtGo2HIkCFYW1vTunVrypYtS3BwcJb6bty4kXHjxuHu7o6Pjw9Dhgzh999/N89v3bo1arU6S+f7hx9+4Nq1awwYMOC5t9GLFpmkwdvJMlrr46gmMsky0nLv/vNx3o5qi3JRSRrc7Kzw87CjU1UvZnQsz4yO5bFWKRnRoDity2d95rWgcvItSUr0HYvIQGY6eQmLco5exVAoVaRE3zFPS4kKx+l+yqyTb0lS7j181tlkMpFy7w5OvpkppZqUJA7+8CEp0RE0f/trXIoWntGZH7hxL4XSnpb/12U8HbgZnWoxrWNNX3ycbdjzblMOT2rByJZl8S/pyuFJLQDoUMOXtlW9LZZRWynR6C1vABU2d5I0+DpZRnB9nW2ISCrcqeTPo3av0XT98rf7P2uxdXYnOerhfpF8LxxbZ3fUdlkv3Jx9S5L8yD6kS08lPSke50ce7XiS48u/5sbBLQ8nmIyYDAas7QruYFNtBr9lHijt7YWbcHB1J/ZumHl+XEQYDq7u2Dpk3VY5ce3kIUpV9X9qinphcCksgfKPZcdUKObC5fCEbMu3q12cW1HJnL8dbzF9ar86TOlb22KajZWKxLTCewNZZG/CBx8/HCxs/3E8PL0IDbllnh96+xYenl44OmWfGaLX6zh6KJjAth2yzIu4E8bIwf2wsrLmhwXL8fbxfWnteBlk2+Sf/B6ArfAmoBfwCLharaZFixbs3r2bRo0aYWtrS5EiRbIt6+7+sIOkUqnMadh3795l69at7NmzxzxfoVCYny1/kM7+wFdffUVwcDDFihV74nc9S7FiDwdw8vHxITY2FpVKxdatW1m6dCmOjo7UqlULeBgl8PDwsKj/08o6Oz88iCiVSpycHj6vqlAoMJlMREZGEhkZSd26dc3z9Ho9LVq0yFLfu3fvMmrUKJT3nxk2Go0UL/4wkunlZfnKHKPRyOeff87evXv55ZdfcHV1fY6t83Jci0lDZzDRtoIHe67FUsHLgco+jmy9HGJRTmsw8XdEMp2rerP0RARONipalnNn97U44tP1vLPpqkX5rztV4Kej4YXqNWSeftVQWau5unsN5Vt2J/raWe5dPkmloH4W5axsbClasxEXNy+hzmsT0KQkciP4D8q37A5A8TotCP7uHaIuncSrfA2u7V2HSm2Lp181TEYDxxZOQ+3gTMCQic98rVBBdfxWPBq9geEtyrDoQAj1y7rTpIInPzwW6Z668RJTNz4cYO6NZqVpUt7D/Boye7WKMa0qEBKTxvV7KXSsWYSaJVz4ZMPFPG3Pi3b5Xipag5FOVbzYejmGyt4O1CjixLRzeTP4ZUFUql4gl3auxq1E5ngHl3b+Sql6gdmWLVmvFX8t/4ritZrg4luKc5sW416qAk7e2Q/y9yj3khW4uncdPhX9sXP15PyWpdi6uONRpvA8f1i1SRuObFiOb+nMwcWObFxB1SZtcr2+iGsXKVah2ouqXr45cCGSdK2B97rX4NuN52lerQhtahXj019PZ1u+QUVvjlzOus8dvRLFwrHN2HjsNmdD4nizfWU8nW3ZeiIsm7WIf5N2HTqzdNE8KlbOPB4sXfQT7Tp0fmL5G9euYjQaqVCxssX0tNRU3h07HP+69Xnnw0/MryAtzGTbiMKgQHfAIfM55V9++YXExEQ6dMh6d+pZvLy86N27Nx999JF52o0bNyhVqhT79++32KEWL15MQkICwcHB2NjYsG/fPqZPn/7c3xkTE0P58pkXHFFRURQrVowTJ06wfPly1q1bh4+PD6mpqRZR5kfr8Txln8TT0xM/Pz82bXo4Omh4eDhublkHefH09GT27NnmVPLExMQnvqJMr9fzzjvvcPv2bVavXo23t3e25fKawQQ/Hg6jVy1fWpd3J0ljYNnJCCKTtZT1sGNkwxJ8vucm8el61pyJ5NUaPnzcpixGExwKSeBwSEJ+N+GFUVpZ03D4FM6s/ZHre9dj4+xG7X7jcfYtSezNCxyZP5XA9+dg7+ZFzVdHcnb9fHZ/PgKFQkHpRu0p3Sgzy8TZtyR1+r/DhT8WkZYQg0vR0jQY9jFKK2vuXTlNfOhVlFZqtn30cARnlbU17aevyK+mPze9wcSbS04zuXMlhjQpTUyKholrz3MzOpXapVyZN8Cfzt8fITLx6a/sW3viDq721vzwei1c7dXcik5l9PK/iUgo3K/6MxhNfLM/hAF1i9K+sheJ6XoWHA0jIklDeS97JjQrzaRt14hL0+V3VfNM5aB+6LUa9nz1FkaDnuL+TanyyCjmO2eMomSdFlRq0wvfSrWp2mEAx5f+D01KIh5lKtNg4Ac5+h6/pq+gy0gleO4kDFoNHmWr0GTElEJ1s6tRt9fRazUsnTwKg15HpQYtaNz94TOWiz8cRuWGgTTo3DdH60uMiaRCQNOXVd08ozMY6fb5Lr4b2pC3O1cjKiGdYT8c4PKdRBpV8mb9xDbUHb+B8NjMTJxS3o6cupF1cMNtJ8P5aNkJlrzdHDdHG87ciuWV6TuITf7vZKj8Vw0cNoqMjAyGvd4TvU5HyzZBDB4x2jx/UJ+utA7qSP9BwwC4GxGOm7sHKivLy/49O7cScSecuNhY9ux8+DaHEiVLM3/ps0cOL4hk2+QhuSeRawpTARzBIjw8nLZt23Lx4kW0Wi1NmzbF1dWVZcuW4e3tTZs2bfj0008ZMGAA+/fv5/bt23z00Ufs2rUry/IPUqR/+uknqlevzq+//sq3337Ln3/+ycKFC4mMjOSzzz4DYObMmYSEhDBr1izi4uIYPnw4cXFxHDhwgNmzZ1uUfZLAwEBKlSrF7NmzuXXrFoMHD2bVqlVERETwySef8Pvvv2NjY8O0adP4/fff2bFjB1FRURb1379/f47Lzp07l9DQUGbMyHw/7BtvvEFQUBBdunQhKCiI0aNH061bN86fP8/gwYNZtGgRNWvWpFq1amzYsIFy5crx9ddfc+3aNWbOnIlCoeCtt96iZMmSTJkyhcDAQGbOnGmOpH/11VccOHCAFStW4JjNszJPM27D5ecq/19hay1HsKfZeiQ0v6tQYNWrJulwT1PksbcgiIfKeMi2eZrxX+/L7yoUWIZrJ/K7CgXWjX3f5HcVRCFW1FX97EIFyNEbCfldBRr4ueZ3FXKlQD8DDplp6M2aNcPb2ztX0dby5cszbdo0Jk6cSJ06dVizZg3z5s3Dzi7rIDCDBg0iMTGRgIAA+vbtS8eOHUlISDA/e51TJUqUoEOHDrz11ltMmzaN8uXL07RpUxo2bEirVq1o27YtdnZ2VKxYkVu3bmVZ/nnKPolarWbevHls2LCBgIAA3n77bT788ENq1qwJQOfOnXn11Vc5evQoo0ePpnjx4nTs2JFWrVrh4eHBe++9l2WdBoOB5cuXc+PGDZo2bYq/vz/+/v68+aaMCCmEEEIIIYQQz1IgI+CF2eMRY/GQRMCzJxHwp5MI+JNJBPzpJAL+ZBIBfzqJgD+ZRMCfTCLg4p8obBHwYzfy/20L9f0K52sXC3wEXAghhBBCCCGE+Dco8IOwFTQTJkxg377s74yPGjUqj2sjhBBCCCGEEKKwkA74c/rmm6enFw0bNiyPaiKEEEIIIYQQeU/ezJZ7koIuhBBCCCGEEELkAYmACyGEEEIIIYTIMQmA555EwIUQQgghhBBCiDwgHXAhhBBCCCGEECIPSAq6EEIIIYQQQoickxz0XJMIuBBCCCGEEEIIkQckAi6EEEIIIYQQIscUEgLP+FPIxQAAbUBJREFUNYmACyGEEEIIIYQQeUA64EIIIYQQQgghRB6QFHQhhBBCCCGEEDmmkAz0XJMIuBBCCCGEEEIIkQckAi6EEEIIIYQQIsckAJ57EgEXQgghhBBCCCHygHTAhRBCCCGEEEL8p0RHRzNkyBD8/f3p0KEDJ0+efGLZwMBAatWqhb+/P/7+/syYMQMAg8HAlClTqFevHk2aNGHVqlXP/F5JQRdCCCGEEEIIkXP/ghz0yZMnU65cOebNm8f27dsZP348e/fuxcrKsouckpJCdHQ0p0+fzjJv6dKlXLlyhT179hAREcGgQYMICAjg/9u777Cmrv8P4O+wp8hGEbeCW1BxK24E96yjVq0TR9XWWket2vq12trWUi3DauvWugeKs27FQcW9QZS9ZSckvz+o0RQS0R/kXpr3qw/PI/cewifvcpOce849t1atWmp/L0fAiYiIiIiISGdkZmbizJkzmDJlCoyMjNCnTx/Y2tri/PnzRdreu3cPtWvXLtL5BoADBw7g448/RoUKFeDm5oa+ffti9+7dGn83O+BERERERERUYhIR/Pf/8ezZM1hbW8PKykq5rXr16njy5EmRtvfv30dubi769euHtm3bYu7cucjMzAQAREVFoUaNGiqP8fjxY42/mx1wIiIiIiIiKlf8/f3h6uqq8uXv76/S5vjx40XauLq6YsWKFTAxMVFpa2JigpycnCK/R09PD40bN0ZwcDAOHjyI5ORkLFu2DACQk5MDU1NTZVtTU1Pk5uZqrJvXgBMREREREVG5Mm3aNEybNk1jmy5duuD27dtFtt+/fx8TJ05U2ZabmwszM7MibYcNG4Zhw4Ypv58+fTrGjx8PoLDTnpeXp9yXk5NT7GO8iSPgREREREREVGISifBfJatTAgMDgyJf1apVQ1pamnIqOQBERkaiZs2aRR5jx44dKiukS6VSGBkZAQBq1KiBqKiotz7Gm9gBJyIiIiIiIp1hYWGBtm3bYtWqVcjPz8ehQ4eQmJiIli1bFmkbFxeHFStWICUlBampqfjpp5/Qp08fAICPjw8CAwORlpaG+/fvY//+/ejZs6fG380p6ERERERERFRi/4G7kGHp0qWYN28eWrdujcqVK8Pf3x/GxsYAgHHjxqF58+aYNGkSJk2ahJSUFPj6+kImk8HHx0c59X3UqFGIjY1Fz549YWBggFmzZqFBgwYaf69EoVAoyvzZEQGYvvee0CWIkonhf+ElrOyEXHwmdAmi1aKhk9AliFqlCsZClyBaNWyZjSYzV54SugTRKnh4VegSROvxqR+ELoHKscoVjYQu4Z3cePZS6BLQpKql0CW8F3bASWtGb40QugRRalG1gtAliFqbKrZClyBaW27GCF2CqOVK5UKXIFpPEoT/4CRmAz14cksd7zrMRp1anWYJXYKo+U4fK3QJorZzjIfQJbwTdsDfH6egExERERERUclxAud74yJsRERERERERFrADjgRERERERGRFnAKOhEREREREZWYhHPQ3xtHwImIiIiIiIi0gCPgREREREREVGISDoC/N46AExEREREREWkBO+BEREREREREWsAp6ERERERERFRinIH+/jgCTkRERERERKQFHAEnIiIiIiKikuMQ+HvjCDgRERERERGRFrADTkRERERERKQFnIJOREREREREJSbhHPT3xhFwIiIiIiIiIi3gCDgRERERERGVmIQD4O+NI+BEREREREREWsAOOBEREREREZEWcAo6ERERERERlRhnoL8/joATERERERERaQFHwImIiIiIiKjkOAT+3jgCTkRERERERKQF7IATERERERERaQGnoBMREREREVGJSTgH/b1xBJyIiIiIiIhIC9gBJyIiIiIiItICTkGn/4zKFYwx2rMKqlY0QXquDH/eiMPV6HS17Y30JZjTuSaO3k/C5Wev23WqbYMebvawMjFAcpYU+27F44qGxykvCmQynN0WgAdhp6GQy1HHswM6DveDvoGhxp+7fmQnnt25jn6z/ldkn0KhwL4f58PcygbdPv6srErXOplMhg0BK3Hxr6OQy+Vo3bEbRvvNhoFh8Vl9Om4wEuNjIJG8Pqe5ducJGBoZaavk95YR9wwRf65GesxTGFtWRH3fj1C5Sdsi7aS52bjx52ok3r8Oib4BqrfqAVfvEZBICqegxd68iDuHNiA3LREVKtdAk8FTUKFSdWSnJuLUiikqj6UokEFeIEP3hethbGmN+6Fb8CzsOKR5ObB0dEF931Gwq91YK8//XTlZGuED90qoYmWCjFwZ9t9OwN8xL4u0MzHQwwfuTqjnYIEChQLnn6bh0N1E5f5JrV1Q194McsXrn1l+6ikSM/OV3xvqSzCtXTX89SgF119klOnzKgtVrU0xtX111LQzQ2q2FL9fjsb5p6lF2jlYGGFi22qo72QJuUKB8OfpCLrwDBm5MpV2lSsYY9XAhlhy5AFuxhbNvLwpkMlwcvOvuHvxL8jlctRr1RFdR01562tyWMifiLx5HUPmLFNuS4l9jqN/+CPuyQMYGBrBrVVHdBo2AfoG5fNjnkwmxeofV+DksSOQywvQqas3pn06F4bFvAbPmTEJEX9fV9mWm5ODcZM/wYjR4/DowX2s+WkFHj64C2NjE7T36oJJ0z6FsYmJtp6OYGq62OHsxtnwGPgN4pPL/zHzNlUqmmBim6qobmOKtBwpNl+NwaWoNLXtjfQlWORdF4fuJKi8NnV3tUPvhg6wMjHE0+RsrA97jsiUHC08g/JFwhno7618vjLT/1tKSgrMzMxg8h95A9LXk2Bmxxr461Eylp94jPpOFpjSthoiU7KRlCUt0r6atSnGeDqjuo0ZcD9Jub1JZUsMbOyElX89xdOUHDSuZImp7aohPjMPz1JztfmUSt3lfRuQEPUII78JhkJegIP+i3Dt8J/w7D282PZ52Vm4tOcP3DixD1UbNiu2TXjoTkTfDodbmy5lWbrW/bkhEE8f3sPKtTtRUFCA7xfNwv4df2DAiHFF2ubm5CDmeRRWbzoIGzsHAap9f3KZFGG/LUG1Vt5o47cUSQ9v4Mofy1HRpTbMbBxV2kbsXAO5NB/dvlyH3IxUXApeBHMHZ7g064SX8dG4vuUHeI5dANsa9fHw1C5c+f1bdJ6zBmbW9vBdtkP5OAXSfJz7ZQ6c6nvCxMoWT8+H4MWN82g3/TuYVrRD1KVQhK1bih5LNr21I6Jt+pLCjvP5yDT8fDYKrvbm+LhlFTxLe4KUbNXXmaFNnWCop4cvjzyElYkBJrepivjMPFyNLuxIV6loAv9zz/BUzYc6l4om+KCpE6pam+Kvsn5iZcBAT4KvvOviyN0EzD14D02dK2But9p4mHgTCW+cZACAud1q40lyNkZv/huGehJ82rkWpnWojqVHH6k83uwutWBi+N+ZuHdu1x+Ie/oQ41b8BnlBAXb/+BUuH9yBNv1GFNs+LzsLZ3f+jmtH96JGo+Yq+w6sWYZa7q0w5PP/ISs9FduXzcFV691o2WuINp5KqVsftAYP7t3BH9v3oaCgAAtmT8e2jevw4diJRdou/ylA5futG9fh+JGD6D94GKRSKebO8kOvfoOw4ucApKakYOGcGVgb8DOmzPhcW09HEIO6e+C72YNgY2UudClaYaAnwdyutXDsfhIWHX6ARpUr4NNONfA4OVvlxOYrNW1NMbFNNdSyM8OhO6+3t6pWEaNaOOPbE49xJy4TXeva4SvvOpix+w7S/3VSkOh9ifKd7Pnz53B1dcXgwYOL7AsKCoKrqysuX74Md3d3xMfHa6Umf39/zJ8/Xyu/Sxt69uyJtLS0d/65xMRETJgwAc2aNUP79u3x66+/ln5x76GegzlMDPUQcjcRBQrgZmwmbsa+RMdaNkXaVrU2wWedauD801QkZam+KFc0NUTI3UTlh+KI2JeIychFLVszrTyPsnT33DE09xkKswoVYV7RFp69R+DmqYNq2+/5bg6yM1LRqFOvYvfHP32A22eOoI5nh7IqWTBnjh5A3w9Go0JFa1jb2mHAiPE4dnBXsW2jntyHVUXrctf5BoCkRzchy81B7U4DoKdvAAe3ZnBw80DUpaMq7WR5uYi5cR6u3sNhYGwKC/vKqNWhDyIvHAYARF89CXtXd9jXaQI9A0PU7ToU0twsJD68UeR33j28EQZGJqjb/QMAQPU2PdFx5o8ws7aHNPslpNkvYWhmqTKbQCzq2JvD2EAPxx8kQ64A7iZk4U58JtpUr6jSzkhfAnfnCjh0NxH5BQokZknx1+MUtK9hDQCwMjGAhZE+nqcXf1KvipUx/Nq4ICw6vUjHvrxoXLkCTA31sPPvWBTIFbgWnY5r0enwrqd6nJgY6CEjV4ZNV18gTyZHZn4BDt9NQD1HS5V2o1u64FbsS8gKFPivuHX2KFr1/gBmFSrCwtoWbfqPRPiJA2rbb1s2G1npqXDv0rvIvpTYaMgLZFC8MaXC0Ni4TOrWhtBD+zD8o3GoaG0DWzt7jPp4Evbv3vHWn3t4/y42/BaAL7/+Dmbm5khMiEPN2nUwcswEGBgYwt7BEd169sKtiL/L/kkIyG9YR3w52ReL16j/e/qvaeBkAVNDfey7GY8CBfD3iwz8/SIDXevaFWlbw8YUC7rXwenHyUjMzFPZ17qGNf56lIJbsZmQK4Cj95OQniND639ev+k1iQi+yivRjoDr6ekhMjISMTExqFy5snL7kSNHYGZW2BkKDw8Xqrxy73063wCwePFiVK1aFatXr0ZiYiKGDBkCDw8PtGzZsnQLfEeVrUwQm5GHNz+axWbkwaWiaZG2iZn5+HTfXeQXKNDD1V5l3+nHKSrf21sYwdnKpNxPPcrLzkRWegpsKldVbrOu5IKstGTkZmbAxKJCkZ/pNX0RLKztcGnvRqQnxqrsy8/NwdG1K9B17CzcvXAcBdLy2UkoTlbmS6SmJKFK1ZrKbc4u1ZGanIjMjHRYVLBSaf/04T0YGhpj4YyxiH0ehcou1TF83DS4Nmiq5crf3cuEaFg4VIFE73Vn18KhCjJiIlXaZSXFQCEvgKWji0q7l7FRAIDM+OewrFRNuU8ikcDCrjJexkXBwdX99e+Le4bI8yHw+uxn5dR1iUQCA2MTxNw4j6sbV0BPTx/NR82Bnr5+WTzl/xcnSyPEZ+arvM7Ev8yHs5VqR8fewgj6ehLEvcx7o10eKlUobOdS0QS5MjnGt6yCKhVNkJYjQ8jdRNyKywQAJGVJ8VXoI+QXKNCpmJOI5YGLtQmep+WqZPU8LQc1bFRPZubK5Pjq8AOVbS2rVcSjpCzl981crNCokiU+3XsHvRqozswor3KzMpGZlgI759fHjW0lF2SmJiMnMwOmxbwmD5i5BJY2dji3awPSElRfk9v0G4Ezf/6OSwe2QSGXo26LdnDv2qfMn0dZyHyZgeSkRFSvUUu5rWq1GkhKTEBGejoqWFmp/dlV3y3FkOEfoXrNwp+t7OxSZIT84tnTqOtWv2yKF4ndx8Lx67YzcHHSnU5jlYomeJGu+przIj0X1ayLfg6Mf5mHyX/eQp5Mjl71VU8K6qHwdelNcoUClSuU3xNaJD7iG2L4h0QiQadOnXD06OuRmGfPnkFfXx92doVns1xdXREXF4fLly/jgw8+wLx58+Dh4QEfHx+EhYUpf+7MmTPo3bs3PD094efnh+TkZACFo9pTpkxB586dMWzYMABAYGAgvL294e7uXuRxSqJz585YvXo12rRpg3bt2mHHjtdnbE+dOoUBAwagefPmaNeuHTZt2gQAuHz5MgYMGIBhw4ahdevWSEpK0th22LBhmD9/Ptzd3dG/f3+Eh4dj+PDhcHd3x/Tp0yGXF75wREZGYvTo0WjRogUGDhyIiIgIAMCIEYXT27y9vXHnzh1IpVIsX74c7du3R8eOHREUFKTyfL788kt4enpi8+bN+OGHHzBnzhzo6ekhKalw6naFCkU/KGibsYEe8v/1gplfIIeRQdHzYzlSOfJLMIpiY2aIWR2r4+yTVLXTRMuL/NzC+g2MXl9y8Orf0vy8Yn/GwrroWeNXTm30R11PL1Sq3aAUqxSH3JxsAIDRG5dnvPp3Xl7xI5Y169bDlDlLsGbLYbRs3wXfzv8ESQlxZV/s/1NBXi70jVQ/VOgbGqFAqvo3IcvLhUTfAHr6Bm+0M1a2k+XnQt9Q9Xp3fSNjFPzrb+vB8T9RxcMLFvbORWpxbOCJXst3odHASbi6cQVexj//fz23slDc64y0QA4jfb0i7WRyhcr13fkFCmU7Az0JolJysPdWAhYcfohjD5Iw1tMZLhUL/85yZSV7jRIzEwN95P0rqzyZHMYGmj92DGzihLY1bLD24jMAgLWpIaa0r47vTz6GTF6+M3mT8jX5jVHqV/+W5hX/mmxpo/41GZCg3YAPMXPtfny8fC2Snkfh4v4tpVavNuVkF74Gv3mN9qt/5+Wqfy++evkCoqMiMWT4R8Xul8vlWPXdUkRHR+GjjyeVYsXiE5eUAYXiv3O8lISJoX7Rz4FqXnOypfIir0+vXIpKQ+c6tqhjZwZ9CdClji2crUxgqC/aLhOVQ6IdAQcKp0kHBQVh9OjRAICQkBD4+Phgy5aibyrh4eHo3bs3Fi9ejODgYCxatAghISGIjo7GrFmzsGbNGjRt2hT+/v74/PPP8dtvvwEArly5gv3798PCwgKXL1/Gjh07sG3bNtja2uLnn3/GypUrsX379neq+/Tp08rfPWrUKDRo0ADVq1fH7NmzERwcDHd3d5w7dw6TJk1Cv379AAC3b99GYGAgmjdvDolEorHt9evXMXToUCxZsgQTJkzAhAkTsGXLFlhbW6Nfv364ePEiWrZsicmTJ2Pw4MEIDg7G6dOnMXnyZISGhmLz5s1wdXXFkSNH4OTkhDVr1uDGjRvYt28fpFIpxo0bB2dnZ/j6+gIAsrOzce7cOUilUhj9s6jU8OHDce3aNQwYMAD16tV7j/+7pStPJi/y4mikr6f2BfZtXO3N4deuKi5FpmFreOzbf0CETm5YhfsXTwKA8o1Y9kbHSpZf2Jk0Mil6dliTuxeOIyMxFt0+/rSUKhXe2lX/w9kThdOp8U9W+W98CM7PLczKxLTopQje/T6Ad78PlN/7DBiOk4f3IuLaJXTu2a/sii4F+kYmKJCqXoZRIM1XOVEDAAZGxv8snFagHJkukOZB38hUuV/+78fJz4O+8evHkeZkITbiPNrPWFl8Lf9c712tZXdEXjiMhHtXYelY5f/3BEtZvkxRpLNtWMzrTL5MDgM9CfQkUHbCjfQlynZ/x7xUWbgt/MVLeFbNQuNKlohOK99rTbySJyuAkUHRExM50uJfkw31JZjavjqaOFfAFwfu4vk/OczqVBN/hsf8J3IJXfcTbp8/obJNlv/6uJH985rzrq/JsU/u49LBbZi+Zickenqwc66Gtv1H4tTWILTpW/z15GLzw7dLcOzIP5dE/XPM5L3xGpz3z2uwqZn6y8EO7d+N7j69YW5hUWTfy4x0fLNwDhIT4vFz4B+wsdV0MoPKozxp0ZOhRgZ6yFXzmqPO+aepsDI1wPSO1WFqqI8LT1MREZOBrHxe/11EeZ4DLjBRn85p27YtHj9+rLzOOzQ0FN7e3sW2tbCwwIgRI2BoaAhfX188e1Z49vzQoUPo0qULPD09YWRkhGnTpiEsLEw5etu0aVM4OTnBwsICjRs3xpYtW2BnZ4f4+HiYmZkhMTGx2N+niZ+fHypWrIhGjRrB29sbR48ehYmJCfbu3Qt3d3ekpqZCT08PMpkM6emFq2ubmZnBy8sLFhYWb21rZWWFfv36QV9fH+7u7vD09ESdOnVgZ2eHunXrIjY2FhEREcjLy8PYsWNhaGiIrl27ombNmjhz5kyRevft24fp06fDxsYGjo6OGDt2LPbs2aPc37VrVxgZGcHc/PVCHuvXr8fx48dx/fr1Yk+IaNuL9Fzl9M5XKlUwxov04kcSNOlQ0xozO1bH7oj4ctv5BoDOoz7B5F/3YfKv++AXsB/mVjZIjY1W7k+NjYa5lQ2MzYp+WNHk/sWTSIp+iqBpgxEwZQDunDuG+5dOYfPC8juiMO6Tefhj/9nCrwPnYG1jh5joSOX+F9GRsLaxg7mFZZGfPbJvO26Gq86UkUmlMDQU/wrolk5VkZn4QmWkJDPhOSydXFTamds7Q6Knj8zEF6rt/pmSbulUFS8TXo9YKxQKZCa+gKXj60se4u9ehZldJVhVrqHy2HcO/oE7h/5Q2SaXSWFoIr6Fg2Jf5sHBUvX/q6OlkcpUcwBIyMxHgVwBBwujN9oZK9s1q1IBTSur/i0Z6Ekgk7/fCUMxikrNQZWKqidyqlQ0xbO0oiOYFUwMsLxPPThbmWDm7jvKS37sLYzQsJIlRrd0wfbRHtg+2gNGBnr4qmddDG5aSSvPozT1GDsDs347oPyyqGiDlJjXr8nJsdGwqGgDE/N3e01+mZwIuawAijcm3+rp64tuEUNNZn2xEIf/Civ8Oh0GWzt7PIt8qtz/LOopbO3sYWFZ/Iw7mUyKS+fPoHN3nyL7Yl5EY/KY4TAwMMQvwZvg4OhUZs+DhBOdloPK/7ocyNnKRHkyr6QqmhrgWnQ6pu26g3HbbuL3sOdwsTbF46Ts0iyXdJyoR8CNjIzg5eWF48ePo02bNjAxMUGlSsW/6drYvL5OTl9fXzkNOzY2FiEhIThx4vVZZ4lEgpiYGABQTmd/5fvvv8eZM2fg7Oys9ne9jbPz6+mVjo6OSE5Ohr6+PkJCQrBhwwZYWFigadOmAF6PTNra2qrUr6ntm1O+9fT0YGn5+oOcRCKBQqFAXFwc4uLi0Lz565VSZTIZvLy8itQbGxsLPz8/6P1zHahcLkeVKq9Hnuzt7Yv8jLGxMVxcXDBixAicOXMGw4cXv5K2ttxLyEJ+gRy9Gzgg5G4i6juao3HlCtgT+vCdHqdJZUt82NwZP5yOxN34zDKqVhhubbshbP9mOFSvAwAIO7AFbm27vfPj9PtU9XZkJzesQoFU+p+6DVmHbr2wa1MwatZxAwDs3rwWHboVvxhdanISjh3YiTnfrIKNrT0O7tyE3NxsNGst/sXp7Go1hL6hER4e34HanQYg6VEEEu5dg1sP1ePZwNgElRu3wd1Df8Bj+CzkZabj8Zn9qN1pAADA2cMLZ1d9ioR712BXuzEentoFfSMT2NVqqHyMlMi7sK1RdLaMTY16uL7lB1Ru3AYVKtfE03MHkZeZDscGwq4rUZyHiVmQFijQw9UWxx8ko669Oeo7WuD7u09V2uUXKPD3iwz0aeCADVdjYGmsD69aNjjxsPDyJ2MDPfjWs0dCZj5iM/LQ3KUCatiYYWu4+C9bKKmbMS+RL5NjqHtl7LoRiybOFdDcxQqbrqheWqAnAb7yrouMXBm+CX2oMs08MTMf/X+7qtJ+z8fNsfjwf+M2ZA3bd8f5vZvgWKPwNfnC3s1o2L77Oz+Oc90G0NPXx+nt69Bh8BhkpiXj4r4tqN+6U2mXrDU9fPpgw7oAuNYrvFZ7w7pA9PBRf03744cPIJfLUddV9TUmOysLs6dNgHvzlvh07lfKtSfov+d2XCbyC+QY2MQJ+27Go2ElS7g7V8D26/ff6XEaVrLEMI/KWHDoAbLyZRjctBIK5AqNt7XVVRIOgb83UXfAgcLrlH///Xekp6fDx6fomc23sbe3x9ChQ7FgwQLltsePH6NatWo4ffq0yovx+vXrkZaWhjNnzsDY2BinTp3C119//c6/MykpCXXqFL6hxsfHw9nZGVevXsWmTZuwa9cuODo6IisrS2WU+c063qWtOnZ2dqhVqxYOHHi9Aubz589hbV10QQ47Ozv4+/ujQYPC63nT09ORm1v8GcMRI0Zg9uzZypMCeXl5KicAhFIgV2DlX0/xUXNn+NSzR3qODEEXnyEmIw917c0wq2MNzAt58NYVhXvVd4C+RIJP2ldT2b7nZjxC37hdWXnUsu9IyPLzsHXRFMhlUtTx9EKrfqOU+zctGA/XVp3RotcwAasUh0EfTkBeXi6+8BsBmVSGNl7dMfij1yP8n40fgradvdF/2FgMHjURMmk+vpoxFtnZWahZtx7m/u8XmL3jKJYQ9AwM0Wr8IkTs+hWPTu2GsaU13IfNhKVTVSQ/uY1LwYvR6fPVMLO2R+NBk3FzTxBOLJsISCSo3qYnqrcunJFUwakqPEZ8itv71yE7LQlWlaqj5biF0HtjBC47JQEVXWoXqcGpgSfq9xqNqxu/gzT7Jayca6HNpG9gXMwiVEIrUABrLjzD0CZO6FLHFi/zZNh4LQZxL/NRy9YUk9tUxdLjj5GaI8P2G3EY1NgJC7vXgkIBnHuaivORaQCAC5FpMDfSx8TWLjA30kf8yzwEXIwutyueF0cmV2BhyH34ta+OQU0rITVbipWnniA6LRcNnCyw2McVk3fcRJWKJnBztECeTI5toz2UP58vk2P4hv/2QqttB3wIaV4ufp8/GQUFUtRr5YV2A19fv7x2zjg0aNMZrftqPsFtbmWNIZ8vw6mtQfD3GwwjU1M0bNcNbft/WNZPocx8NN4Pubm5GP/hYMikUnTq5o0xE6co94/+oB+6evti5OjxAIDYmOewtrEtct/zE0dDEPPiOVKSk3HiaIhyu0vV6gja8PZV1an8kMkV+OboY0xo7YK+DR2RliOF/9koPE/PRT1Hc8zrVhsz99wp9ta0bzr3JBVVK5piRR83GOlLcD8xC4uPPIS0nK/LQeIi+g54u3btMHfuXMTHx2Pjxo3v/PM9evTAqFGj0KdPHzRq1Ajbt2/Hjz/+iL/++qtI28zMTBgaGkJPTw/x8fH46aefIH2P1Z2DgoLQqFEjPH36FMeOHcPWrVsRExMDPT09GBkZITs7W9mxl8mKXlOSlZVV4rbqNG3aFFlZWdi1axf69++PW7duYcyYMVi3bh2aNGkCQ0NDZGYWjvD6+vrC398fK1asgEQiwcyZM1G1alUsWrSoyOPWq1cPa9aswU8//YTY2Fhs3rz5vU5SlIXYjDx8e/JJke0PErMxaeftYn/mswP3VL5fevxxmdQmBgaGRug4fDI6Dp9c7P6R3wQXu71VP80f4jqP+uT/XZvYGBoZYbTfZxjtV/yo/vfBrz+4GRgY4MOJM/HhxJnaKq9UWTq6oK3f/4pst63ZQOX+3YamFvAYPkvt41Rq2AqVGrZSu7/VuIVq91Vv7a3szItd/Mt8/HzuWZHtj5Nz8NmB1yMtOVI5Nl6LUfs4xx4k49iD5Lf+vkVHy+9rUnRaLub+6zUWKBypGrTuGoDCUW7fwJIvdvrvEfHyzMDQCF1HTUHXUVOK3T9u+dpit7cbOKrItkq1XDF8QfHrK5RHRkZGmPbpF5j26RfF7v99216V77269IBXlx5F2vXuPxi9+xe9pa2ueBabAlP3qUKXoTUv0nPx1ZGiMx/vxmfhw01Fb4sJAH7FfD7ccj0GW66rf/0m+v8S9TXgQOGLcIcOHeDg4AAHh3e/z26dOnWwZMkSzJs3D82aNcOOHTsQEBAAU9Oii5yMHj0a6enp8PT0xLBhw+Dr64u0tDTltdcl5eLiAh8fH3zyySdYsmQJ6tSpg/bt26N169bo0qULunfvDlNTU7i6uuLp06dFfv5d2qpjZGSEgIAA7N27F56enpgxYwbmzp2LJk2aAAD69OmDQYMG4dKlS5gyZQqqVKkCX19fdOnSBba2tvj888+LfdyZM2fCxsYGnTp1wsSJEzFlyhS0b9/+nfIhIiIiIqLySyIR/qu8kih07T4FZaxz585YsWKFyrXXVGj01gihSxClFlXFN9VWTNpUsX17Ix215SbP0Gvyrqvf6pInCeX/GuqyNNCDC3Wp412H2ahTq5P6WUIE+E4fK3QJorZzjMfbG4nIowThb9Fb2+Hd7hohFqKfgk5ERERERETiUY4HoAXHDvg7mjVrFk6dOlXsPj8/Py1XQ0REREREROUFO+Dv6IcfftC4f/z48VqqhIiIiIiIiMoTdsCJiIiIiIio5DgH/b2JfhV0IiIiIiIiov8CjoATERERERFRiUk4BP7eOAJOREREREREpAXsgBMRERERERFpAaegExERERERUYlJOAP9vXEEnIiIiIiIiEgLOAJOREREREREJcYB8PfHEXAiIiIiIiIiLWAHnIiIiIiIiEgLOAWdiIiIiIiISo5z0N8bR8CJiIiIiIiItIAdcCIiIiIiIiIt4BR0IiIiIiIiKjEJ56C/N46AExEREREREWkBR8CJiIiIiIioxCQcAH9vHAEnIiIiIiIi0gJ2wImIiIiIiIi0gFPQiYiIiIiIqMQ4A/39cQSciIiIiIiISAs4Ak5EREREREQlxkXY3h9HwImIiIiIiIi0gB1wIiIiIiIiIi3gFHTSmrY1rYQuQZRO3k8VugRRszEzFLoE0apjZyJ0CaKWlCUTugTRcrC0EboEURvuXlXoEkQrJTNf6BJEy3f6WKFLELVDP68TugRxG+MhdAXviHPQ3xdHwImIiIiIiIi0gCPgREREREREVGJchO39cQSciIiIiIiISAvYASciIiIiIiLSAk5BJyIiIiIiohLjDPT3xxFwIiIiIiIiIi3gCDgRERERERGVGBdhe38cASciIiIiIiLSAnbAiYiIiIiIiLSAU9CJiIiIiIioxCRchu29sQNOREREREREOiUxMRFz5sxBeHg4KlWqhK+//hrNmjUr0m7cuHG4du2a8nupVAoAuHXrFqRSKdzd3WFoaKjcP3PmTIwaNUrt72UHnIiIiIiIiEruPzAA/uWXX6J27doICAjAkSNHMHPmTJw8eRIGBqpd5LVr1yr/LZVKMXz4cPTr1w8A8PjxYzg5OeH48eMl/r28BpyIiIiIiIh0RmZmJs6cOYMpU6bAyMgIffr0ga2tLc6fP6/x59avXw8LCwuMGDECAHD//n24ubm90+9mB5yIiIiIiIh0xrNnz2BtbQ0rKyvlturVq+PJkydqfyY9PR1BQUGYP3++ctv9+/cRExMDb29vtG/fHsuXL1dOUVeHHXAiIiIiIiIqMYkIvvz9/eHq6qry5e/vr1Ln8ePHi7RxdXXFihUrYGJiotLWxMQEOTk5ap/z9u3b0bp1a9SuXVu5zdTUFM2bN8f27duxY8cOhIWFITg4WGN2vAaciIiIiIiIypVp06Zh2rRpGtt06dIFt2/fLrL9/v37mDhxosq23NxcmJmZqX2sAwcO4LPPPitSwytWVlaYMGECgoOD4efnp/ZxOAJORERERERE/zkSiQQGBgZFvqpVq4a0tDRkZmYq20ZGRqJmzZrFPk5sbCxiYmLQtm1ble1BQUF4/Pix8nupVAojIyONNbEDTkRERERERCUmkQj/9f9hYWGBtm3bYtWqVcjPz8ehQ4eQmJiIli1bFts+IiIC9evXL7JC+uPHj7Fy5UpkZWUhJiYGgYGB6NOnj8bfzQ44ERERERER6ZSlS5ciKioKrVu3RkBAAPz9/WFsbAyg8N7fAQEByrYxMTGwt7cv8hjz5s2DiYkJOnXqhIEDB6JLly4YOnSoxt/La8CJiIiIiIioxCT/gRuB29nZISgoqNh9b977GwDGjBlTbDsrKyv88MMP7/R7OQJOREREREREpAXsgBMRERERERFpAaegExERERERUcmV/xnoguEIOBEREREREZEWcASciIiIiIiISowD4O+PI+BEREREREREWsARcPrPKJDJcGpLAO5f+gtyuRxuLTui84d+0Dcw1PhzVw7vRNSt6xg0+3/KbdK8XJzevhaPr1+ANC8PNZt4ouuY6TAyNi3rp1EmqliZ4OPWVVDd2hRpOTJsvR6DsGfpatsb6UuwoHttHL6biIuRacrtVa1N8FGLKqhqbYKs/ALsvRmPvx6laOEZlK0CmQxHNqzGrYunoJDL0aB1J/QcPRUGxfztKBQK/LXzD/x9+ghysjJhV9kFXYeNR82GHgCAJzev49jWICTHRMPEzALunXqi48BR0NMrv+c7C2Qy/LUlAPcu/wWFXA7Xlh3ReeTbj62rh3ci6vZ1DPxM9dg6s30tHoVfgCwvDzWaeKLb6OkwLCfHlrxAhrCdQYi8egYKuRzVm7WH55BJarO4f+YQbh7dgbzMDDjUaoA2Iz6BuY094h/dwvHVC1UfWyYDAHzov19le/yj2wj9aQ4GLP4NFraOZfPESom8QIarO4MRde0MFPICVGvWAc0HT1Sbz4OzIbgdugO5WYX5tBoxHebWhfdZjbp2Fud+XwF9AyNl+3pd+qNJr5EAgANfT0JmcgIkktfjMINXbIO+oea/S7GSSqX4bvkyhB4+hAK5HD28e+KLuQtgaGRUpG1BQQGCA3/Fnl07kZWVCXeP5vhi/gI4O1cRoPLSJ5NJsfrHFTh57Ajk8gJ06uqNaZ/OhWEx/2/nzJiEiL+vq2zLzcnBuMmfYMTocXj04D7W/LQCDx/chbGxCdp7dcGkaZ/C2MREW0+nVFWpaIKJbaqiuo0p0nKk2Hw1Bpei0tS2N9KXYJF3XRy6k4DzT1OV27u72qF3QwdYmRjiaXI21oc9R2RKjhaegXjUdLHD2Y2z4THwG8QnvxS6HNIh5fcTIf2/pKSkIDc3V+gyStWFPRsQ//Qhxny7FmO+DUZ81COEHdqhtn1edhZObFyN01uL3v/vcPD3SE+IxUdLA/Hxd+uRnhSP8zt/L8Pqy46+ngSzO9dA+PMMjNt2E7+HPcfktlVhZ170Qx0A1LAxxVfedVDH3lxle0VTAyzsUQfnn6ZiwvZb+PX8M4zxrIJq1uWj46TJqT9/R+zTh5i68ndM+X49Yp8+wPn924pte+XoPkScPYYP532HL37bh+Zd+2DrdwvwMjUZ2S/TseW7+WjTawjmrj+IMYtW4drJQ7h14ZSWn1HpurBnA+IjH2LMsrUYvSwYCZGPcOUtx9bJTatxelvRY+tI8PdIT4zFR98EYuyK9chIise5cnRs/X1wE1KePUK/hYHouzAAydGPcevYzmLbvrhzHeEHNqLzxIUYumIbLGwdcX7jjwAAx9oNMeLH3cqvgV+vh6mVNVqPmK7yGHnZL3Huj++hkMvL/LmVhhsHNyMl+hF6fxmAXl8GICX6Me4c21Vs25i713HjwEZ0nPglBi/fCnMbB1z8Jx8ASIl+hFqtuuGDH3cpv151vmV5uciIf4G+i4JU9pfXzjcA/LraH3fv3MaeA4exZ/8h3L1zB+vXrS227aYNv2Pnjm34yX81Tp25gNp16mDCx6ORl5en5arLxvqgNXhw7w7+2L4Pv2/bh4f372LbxnXFtl3+UwAO/xWm/Br18STUrF0H/QcPg1QqxdxZfmji0Rx7jpzGr+u34t6dW1gb8LOWn1HpMNCTYG7XWrgWnY7Rm2/gt0vPMaV9NdhbFP9+XtPWFF/7uKKug+r7eatqFTGqhTMCLzzD6C03cP5pKr7yrgMrE90ZlxvU3QMn1s2CjZX52xtTsSQS4b/KK1F2wJ8/fw5XV1cMHjy4yL6goCC4urri8uXLcHd3R3x8vFZq8vf3x/z587Xyu7ShZ8+eSEtL+389xpgxY+Dv7186BZWCW2ePomXvD2BWoSIsKtqidd8RuHHyoNr2O779HNnpaWjSuZfK9qy0FDy8eg7dx86EibklTC0qoM/UBXDv3q+Mn0HZaOBkAVNDfRy4lYACBXAj5iVuxLxE5zo2RdpWtzHF3K61cPZxChIz81X2ta9pg0dJWTj5MBkKAPcTsjDv0H0kZJb/D3x/nwlFu77DYF6hIiytbdFxwChcPba/2LY5WS/RYcCHsKvsAj09fXh06gkDQyPERT6CmaUVZgfuQqM2nVFQIMPL1CTICwpgXsFKy8+odN0+dxSevV4fW636aT62/lz+ObLUHVvXzqHbmNfHVu8pC+BRjo6tR5eOo2GPITCxtIKZlQ2a+AzD/TMhatoeQ62WnWHjUgsGRsZo1n8s4h5GICPhRZG2F7f4w7F2I9Ru1VVl+4VNq1CjhVdZPJUy8eTycTToPliZT6Oew/DgbPH5PL54DDXeyMej/1jEP7ypzCc5+hFsqtYp9mdTnj+BsaUVzCraldlz0bb9e3fj43ETYGNjA3t7B0ycPAU7tm8ttu3R0CMYOWo06tVvAEMjI0z7ZCaSk5Jx6eIFLVddNkIP7cPwj8ahorUNbO3sMerjSdi/W/1Jv1ce3r+LDb8F4Muvv4OZuTkSE+JQs3YdjBwzAQYGhrB3cES3nr1wK+Lvsn8SZeDV+/m+m/EoUAB/v8jA3y8y0LVu0eOgho0pFnSvg9OPk5H4r/fp1jWs8dejFNyKzYRcARy9n4T0HBla17DW1lMRlN+wjvhysi8WrzkgdCmko0R7qktPTw+RkZGIiYlB5cqVlduPHDkCMzMzAEB4eLhQ5ZV7/9/O944dO3Dp0iV4eHiUTkH/T7lZmchKS4Gtc1XlNpvKLshMTUZOZgZMLSoU+Zl+MxbD0sYO53dvQHpinHJ7fNQjmFvZ4OG187gWuhuy/DzUbd4eHT4Yp5XnUtqcrUzwIiMXije2xaTnomoxI9fxL/Mwbfcd5Mnk8KnvoLKvpq0pEl7mY2r7amhcyRJpOVLsvBGHF+nluwOek5WJzNRk2Feprtxm51y1cEQ7MwNm//rb6TjgQ5Xvo+7dRF5OFhyr1QIAGJsWvj6tGN8f+TnZaNS2C2o0FMdx8j6KPbYquSAzTf2x1feTwmPrwp7ij61Hr44taR7qNG+PDkPLx7GVn52JnPQUVHR6nYWVowty0pORl/USxuaWKu3T46JRybWJ8nsjU3OYVrBG6otIVHBwVm5/cec64u5HYMCS31R+/t6ZQyiQSlGvY2/cPLK9jJ5V6Sk+nypq88mIi4aTa1Pl96/ySYspzCc1+gn09PRx68g2SCQSVPVoj6a9P4S+oRFSoh9B39AIoSs/Q0b8C1RwrAL3fmPgUKu+tp5uqcrIyEBiYiJq1qqt3FajRk0kJiQgPS0NVhUrqrRXyOUwNX39Gi6RSKCnJ0FUZKSWKi47mS8zkJyUiOo1aim3Va1WA0mJCchIT0cFK/UnNFd9txRDhn+E6jULf7ayswuW/xSg0ubi2dOo61Y+/06qVDTBi3TV9/MX6bnFzkSLf5mHyX/eQp5Mjl7/ej/XA5ArU51VI1coULmCcRlULT67j4Xj121n4OKkGyccyoqEy7C9N1GOgAOFbyadOnXC0aNHlduePXsGfX192NkVnulzdXVFXFwcLl++jA8++ADz5s2Dh4cHfHx8EBYWpvy5M2fOoHfv3vD09ISfnx+Sk5MBFI5qT5kyBZ07d8awYcMAAIGBgfD29oa7u3uRxymJzp07Y/Xq1WjTpg3atWuHHTten7E9deoUBgwYgObNm6Ndu3bYtGkTAODy5csYMGAAhg0bhtatWyMpKUlj22HDhmH+/Plwd3dH//79ER4ejuHDh8Pd3R3Tp0+H/J+pipGRkRg9ejRatGiBgQMHIiIiAgAwYsQIAIC3tzfu3LkDqVSK5cuXo3379ujYsSOCgoJUns+XX34JT09PbN68GQAQFxeHP/74A926dXunbMqSNK/wuiVDo9fXdL36tyy/+A6ipU3xIye5mRnISk9B3JP7GPX1Goxc5I+Yx3dxdkfx09/EzsRAD/n/eqPNk8lhZFD08M+RypEnK36qq7mxATrWtsHlqDRM/vMWtl6PxZR21VDTtnxPQc/PffW38/qDx6t/S98ynTMu6jH+/GkxOg8Ziwr/+nuaHbgbU1auR/TDOzi9a0MpV609ZXVsffj1Goz4yh+xj+/i3J/l49iS5hVetmPwxt/Kq38Xl4UsL0el7av2/257I2Qz6nftr9JBTY2Jwu1jO9F21MxSq7+svcpH/43nrK8hH2lebpF89P/JJz87E1aVqqKaR3v0+SoIXaYtRey9cFzfu17Z1rZqbbT56FMMWLoBVd3b4tSahchKSSiLp1bmcrKzAQAmpq+Ps1f/Lu5ysS7demDThj/w5PFj5Ofn49fV/sjNzUW+mmOyPHmVxZvXaL/6d16u+muUr16+gOioSAwZ/lGx++VyOVZ9txTR0VH46ONJpVix9pgY6hd5P8+XyWFczPt5tob380tRaehcxxZ17MygLwG61LGFs5UJDPVF2y0oVXFJGVAoFG9vSFRGRDsCDhROkw4KCsLo0aMBACEhIfDx8cGWLVuKtA0PD0fv3r2xePFiBAcHY9GiRQgJCUF0dDRmzZqFNWvWoGnTpvD398fnn3+O334rHGm4cuUK9u/fDwsLC1y+fBk7duzAtm3bYGtri59//hkrV67E9u3vNvJw+vRp5e8eNWoUGjRogOrVq2P27NkIDg6Gu7s7zp07h0mTJqFfv34AgNu3byMwMBDNmzeHRCLR2Pb69esYOnQolixZggkTJmDChAnYsmULrK2t0a9fP1y8eBEtW7bE5MmTMXjwYAQHB+P06dOYPHkyQkNDsXnzZri6uuLIkSNwcnLCmjVrcOPGDezbtw9SqRTjxo2Ds7MzfH19AQDZ2dk4d+4cpFIpAGDhwoWYNWsWTp48+R7/V0vPsfWrcOfCCQBQvpC++SFPml/4oeVdF3fSNzSCQi5Hxw/Gw9jUHMam5vD0HYK/tgSg88jJpVS99uTK5DD615uqsYEecqXvdk2prECOO3GZuPLP4m3hLzJwK+4lmrlY4Uly+Vq45cDaHxFx9pjKNtW/ncJ/G5mo/9u5fekv7A9ciQ4DRqJt76FF9hsaGcHeuRpa+wzEtZMh6DR4dOkUrwXHfl+Fu2V4bHX417F1aksAOo0Q57F1cYs/nlz55xr+V1lIX1+e8SoXQ+OiCzoZGJmgIF/1Ug5Zfp5K27TYZ0iOeojOExeqtDm7bjlafuAHU8uKyEkX70KHl7f+gqdhhfko/hmXK3gjnwJN+RiboECq2mEsyM+DobEpjMws0H3mcuX2Co7OaOT9Aa7tCkKLwRPh5tUHbl59lPvrde6HRxeOIuZuOOq07VF6T7AMfb14IQ4dKJwC+yq7vNzXeeTmFB5npv/M+nvTR2PGIicnG5MnfAwFFOg/YBBq1aqNChWKzkgpD374dgmOHfnnspZ/+kVvXs+el6s+i1cO7d+N7j69YW5hUWTfy4x0fLNwDhIT4vFz4B+wsS2fly7kSYu+nxu9x/v5+aepsDI1wPSO1WFqqI8LT1MREZOBrHxZaZZLRGqI+lRX27Zt8fjxY+V13qGhofD29i62rYWFBUaMGAFDQ0P4+vri2bNnAIBDhw6hS5cu8PT0hJGREaZNm4awsDAkJSUBAJo2bQonJydYWFigcePG2LJlC+zs7BAfHw8zMzMkJia+c91+fn6oWLEiGjVqBG9vbxw9ehQmJibYu3cv3N3dkZqaCj09PchkMqSnF3ZmzMzM4OXlBQsLi7e2tbKyQr9+/aCvrw93d3d4enqiTp06sLOzQ926dREbG4uIiAjk5eVh7NixMDQ0RNeuXVGzZk2cOXOmSL379u3D9OnTYWNjA0dHR4wdOxZ79uxR7u/atSuMjIxgbm6OvXv3wszMDF26dHnnXEpbtzGf4JPg/fgkeD9mrD0A84o2SI6NVu5PiYmGeUUbmJgXfTPWxKayC4DChaReUcjlKK8nS5+n5aLSv6aVVbYqnMb2Ll6k58HMUF9lm56kfE5A6j1uJub/EaL8srC2RVLM67+dpBfPYGFtC1M1fzt/7dqA/UErMWDqXJXO9/NHd7H6szGQywuU22RSKUzMytciL91Gf4LpQfsxPWg/PgkuPLZS3jy2Yt/v2LJ9dWzlvD625HI5IOJjq/Xwaa8XS/tpD0ytbJAR9zqL9PhomFrZwMisaBYVK1VFevxz5ff5OVnISU+FVaXXU7Sf3biISm5NYWL5elpt8rOHyEiMwZl1K7Dl08HYs3gCAGD/Ur/XJwNEouWwqcoF0Ib9uBumVjZIj38zn+ea84krJh+nwtzC9/2u0l4uk0LvnxXR7/91ALH3/i6yvzwtwvblV0tw6Wo4Ll0Nx+Wrf8Pe3h5Pnz5R7n/69Ans7e2L7VTHx8dh8JAPEHriLxw9cRojPvwIz55FoV79htp8CqVm1hcLXy+kdjoMtnb2eBb5VLn/WdRT2NrZw8Ky+BMMMpkUl86fQefuPkX2xbyIxuQxw2FgYIhfgjfBwdGpzJ5HWYtOy0FlK9X3c2crEzxPe7f384qmBrgWnY5pu+4oF2d1sTbF46Ts0iyX/uOEXoCtPC/CJuoRcCMjI3h5eeH48eNo06YNTExMUKlSpWLb2ti8XlBKX19fOQ07NjYWISEhOHHihHK/RCJBTEwMACins7/y/fff48yZM3B2dlb7u97G2fn1tX2Ojo5ITk6Gvr4+QkJCsGHDBlhYWKBp06YAXo8u2draqtSvqe2bb8Z6enqwtHw9bVEikUChUCAuLg5xcXFo3ry5cp9MJoOXl1eRemNjY+Hn56e8TZJcLkeVKq9vZWJvX3hLmMTERKxZswZbtxa/KIzQGrTrhot7N8GpeuGiPRf3bUaDdu8+Td6+Sg1UquWGv7YEopffXOTn5iDs0A7Ua92ptEvWijvxmcgvUKB/I0fsv52Ahk4WaFLZEn/+HftOj3PmcQp6uNnBq7YNTj9KQVPnCqjvZIGt12PKqHLtadqhO07v2oBKNesCAM7s3oimHboX2zYsdC8uhezE2EWr4Fi1pso+B5cayMvJweldG9FhwIdIiH6KSyE70XXY+DJ/DmWpQdtuuLhvExz/ObYu7duMBm3f/diy++fYOr0lEL6TC4+tK4d2wK0cHVu1WnbBjcNbYFu18FrdiMNbUatl8Scka7XqirPrv0N1j/aoWLkaru1ZB7vqrrByfP36mvjkDhxqNVD5OcfaDTFy1V7l9znpKdgxdyT6zF8j+tuQ1WzZBTdDtsLWpTCfm4e3oqaafGq27ILzv3+Pas3ao2Klari+Zx1sq9dFBUdn5L5Mx4Mzh2BsXgFunfviZWIsbh7ZhjrtCk/CZ6en4MHZQ+jktximVja4e2IPpHk5qNKopdaea2nr3bc/An9djfr1C/8eggLWoHff/sW2PXzoIP46dRJrAtdCIpFg+bJvUNfVDQ0bNdJmyWWmh08fbFgXANd6hddqb1gXiB4+fdS2f/zwAeRyOeq61lPZnp2VhdnTJsC9eUt8OvcrlVvWlUe34zKRXyDHwCZO2HczHg0rWcLduQK2X7//To/TsJIlhnlUxoJDD5CVL8PgppVQIFfgarT625MSUekRdQccKLxO+ffff0d6ejp8fIqe2Xwbe3t7DB06FAsWLFBue/z4MapVq4bTp0+rvBivX78eaWlpOHPmDIyNjXHq1Cl8/fXX7/w7k5KSUKdO4QfV+Ph4ODs74+rVq9i0aRN27doFR0dHZGVlqYwyv1nHu7RVx87ODrVq1cKBA69XeHz+/DmsrYsuOGFnZwd/f380aFD4pp+enl7sNWcXLlxAQkICevQonN6Xm5sLiUSCW7duITAw8K01lbU2/T+ELD8PG770Q4FMCrdWXmg74PW1YOvnjke91p3Rqs+wtz5W/1lf46/NAVj7+RjIZTK4te6EtgOLv65M7ArkCnx74jE+bumCXg0ckJ4rxZrzz/AiPQ+uDub4oktNfLb/HpKzpBofJzotF8uOP8Fwj0r4sLkzUnOkWH02Cs9Sy//t7LwGfQRpXh4Cv5iAApkMDdt0QqfBY5T7V382Bo3adkWH/iNwZu9m5OfmYO2XU1Ueo7/fF6jfsgNGfLEMh3/3x4px/WBuZY2OA0ehiZrOfHnRuv+HkObnYePCwmPLtZUX2rxxbP0+dzzqtemMlr3ffmz1m/k1/toSgN/m/HNsteqkcpyKXVPfkbiWn4cDy6ZBXiBD9WYd4N779cJ8e7+ehJotOqGx91A4128Gj74f4fS6b5GXmQ6HmvXRcfxclcd7mRyPau7ttP00ykxjnxGQ5efh0LfTlfm8unUYABz4ejJqtPBCQ++hqFy/GZr2GYVz65YjLzMd9jXro8O4wnxMLK3QecpiXNv9G24e3gIDY1PUbuuN+l0GAACa9BoBuUyK0JWfQZqTDZtqddBl6tcwMi1fs03eNHnKNOTm5mDooP6QSqXw7ukDv6mvb0vXv48vfHv1xrgJk/DR6LGIefECvXt2g1yuQJu27fDz6l/LfQfzlY/G+yE3NxfjPxwMmVSKTt28MWbiFOX+0R/0Q1dvX4wcXXhyMzbmOaxtbKFvoPqx9sTREMS8eI6U5GScOPp6NX6XqtURtOHtq6qLjUyuwDdHH2NCaxf0beiItBwp/M9G4Xl6Luo5mmNet9qYuecOkt7yfn7uSSqqVjTFij5uMNKX4H5iFhYfeQhpgYinIxH9h0gUIlyF4Pnz5+jevTvu3LmD/Px8tG/fHhUrVsTGjRvh4OCAbt264ZtvvsGoUaNw+vRpREVFYcGCBTh27FiRn3/48CFGjRqFwMBANGrUCNu3b8ePP/6Iv/76C2vXrkVcXByWLl0KAFixYgUiIyOxatUqpKSkYMKECUhJScHZs2fh7++v0ladzp07o1q1avD398fTp08xZswYbN26FTExMfjqq6+wZ88eGBsbY8mSJdizZw9CQ0MRHx+vUv/p06dL3HbNmjV49uwZvv32WwDAxx9/DG9vb/Tt2xfe3t6YMmUK+vfvj1u3bmHMmDFYt24dmjRpgoYNG2Lv3r2oXbs2Vq5ciYcPH2LFihWQSCT45JNPULVqVSxatAidO3fGihUrVEbSX5k/fz6cnJwwbdq0Ev1/Db4cVbI/AB1z8n6q0CWIWp9G9kKXIFov83i9niZJWcxHnQLxvfWLymyv2m9vpKNS/nWLSnpt+p5bQpcgaod+Lh8LbgolJ/wXoUt4J6nZBW9vVMaszfTf3kiERH0NOFA4Db1Dhw5wcHCAg4PD23/gX+rUqYMlS5Zg3rx5aNasGXbs2IGAgACV23e8Mnr0aKSnp8PT0xPDhg2Dr68v0tLSlNdel5SLiwt8fHzwySefYMmSJahTpw7at2+P1q1bo0uXLujevTtMTU3h6uqKp0+fFvn5d2mrjpGREQICArB37154enpixowZmDt3Lpo0KbwlTp8+fTBo0CBcunQJU6ZMQZUqVeDr64suXbrA1tYWn3/++Ts9ZyIiIiIiItJMlCPg5ZmmEWNdxxHw4nEEXDOOgKvHEXDNOAKuHkfANeMIuHocAVePI+CacQRcs/I2Ap6WI/wIeEVTjoATERERERERkRqiX4RNbGbNmoVTp4q/DYyfn5+WqyEiIiIiIqLygh3wd/TDDz9o3D9+fPm+3RAREREREZEmEvw37rogBE5BJyIiIiIiItICjoATERERERFRiUk4AP7eOAJOREREREREpAXsgBMRERERERFpAaegExERERERUYlxBvr74wg4ERERERERkRZwBJyIiIiIiIhKjkPg740j4ERERERERERawA44ERERERERkRZwCjoRERERERGVmIRz0N8bR8CJiIiIiIiItIAj4ERERERERFRiEg6AvzeOgBMRERERERFpATvgRERERERERFrAKehERERERERUYpyB/v44Ak5ERERERESkBRwBJyIiIiIiopLjEPh74wg4ERERERERkRawA05ERERERESkBZyCTkRERERERCUm4Rz098YRcCIiIiIiIiIt4Ag4ERERERERlZiEA+DvjSPgRERERERERFrAEXAiIiIiIiIqMRP2It+bRKFQKIQugoiIiIiIiOi/jlPQiYiIiIiIiLSAHXAiIiIiIiIiLWAHnIiIiIiIiEgL2AEnIiIiIiIi0gJ2wImIiIiIiIi0gB1wIiIiIiIiIi1gB5yIiIiIiIhIC9gBJyIiIiIiItICdsCJiIiIiIiItIAdcNJp2dnZ2L59u9BlEBHpjJiYGJw8eRLR0dFF9h0+fFiAioiIiLRHolAoFEIXQaRtt2/fxvbt23Hw4EFYWVnh1KlTQpckmIsXL+L+/fto06YNatSogf/9738ICwtD06ZNMXfuXFhYWAhdoqDu3buHJ0+ewMfHBwAglUrxxRdfYPz48XBzcxO4OmEpFAqcOnUK169fR2ZmJpycnFCvXj107NhR6NJIpI4dO4Y5c+agRo0aePLkCQYNGoT58+cr93t4eOD69esCVkhiJZPJsG7dOty8eROurq4YNWoUKlSooNw/efJk/PrrrwJWKLwrV67A0tISbm5u2LZtG06fPo0KFSqgb9++aNOmjdDlic6kSZMQEBAgdBmkg9gBJ52Rk5ODgwcPYuvWrbh79y769euH/v37o0WLFpBIJEKXJ4jg4GCsX78e7u7uuHXrFurVq4ekpCT07t0bx44dQ+XKlbFixQqhyxTM9evXMWHCBEycOBHjx48HAGRmZuL777/HgQMHsG7dOjRp0kTgKoWRlJSE8ePH48WLF3B3d0eFChWQlpaGv//+G7Vr10ZQUBAsLS2FLlPrxo4d+9Y269at00Il4uTj44OFCxeiVatWiIuLg5+fH9zc3PC///0PAODu7o7w8HCBqxROfHz8W9s4OjpqoRLxWbJkCe7cuYPevXvj7NmzePjwIf744w9UqVIFAE/eBAYGYtOmTQCADh064OrVqxg5ciTy8vKwefNmTJs2DQMGDBC4SmH88ssvxW4PDg5WvrdPnTpVmyWRjmMHnP7z7ty5g23btuHw4cNo0qQJBgwYgK+//hoHDx6Era2t0OUJysvLC7/99htq1aqFe/fuoX///jh16hScnJyQmpoKb29vXL58WegyBfPhhx9iyJAh6N27d5F9O3bswMGDB7FhwwYBKhPep59+CoVCgWXLlsHY2Fi5PScnB3PmzIG1tTUWL14sYIXCOHz4MObMmYNx48bBxcWl2Db9+/fXclXi0axZM1y7dk35fXp6OoYPH46uXbti5syZOt8B79SpE+Li4gAUzjD5N4lEgrt372q7LFFo27YtDh06hIoVKwIo7FTt3bsX27dvh62trc7/7XTs2BHbtm1DYmIihg4diuPHj8PZ2RkA8PjxY0yePBlHjx4VuEphjBkzBmFhYejWrZvKrL59+/ahb9++AIBvvvlGqPJIBxkIXQBRWRswYAD69euHgwcPKkcOXo226Lr09HTUqlULAODm5gYTExM4OTkBAKytrSGTyYQsT3APHjxAr169it3Xv39/fP/991quSDwuXLiAI0eOqHS+AcDU1BQLFizAkCFDBKpMWD179kRCQgJCQkK4vkQxateujV27dmHgwIEAACsrKwQHB+ODDz6AsbGxzs5GemX37t0YOXIkJk2aVOyJP12mUChgaGio/H7q1KlIS0vD+PHjsXHjRgErE4esrCw4OjrC0dERHh4esLe3V+6rXLky0tPTBaxOWOvXr8eOHTuwYcMGfPrpp+jUqRMA4OjRo+x4kyC4CBv9533xxRe4d+8ehg8fjl9++QXPnz8XuiTR+PeH3Tc/3ADFj8DoEn19fWRnZxe7TyqVQk9Pd19Cc3NzYWVlVew+BwcHZGRkaLki8Rg1ahTat2+PpKQkoUsRnfnz5+Pnn3/G9OnTldsqV66M9evXY/fu3cjJyRGwOuFZW1vj22+/xbJly5CVlSV0OaLSsWNHzJw5U2WUe/78+XBxccHIkSNRUFAgYHXCa9asGb799lsoFAps3rwZRkZGAICIiAjMnDkTnTt3FrhCYQ0ZMgRr167Ftm3b8OmnnyIlJUXokkiH6e6nR9IZo0ePxt69e+Hv74+0tDQMGTIEycnJ2Lt3r85/QJbJZDh48CAOHDiAAwcOQCqVKv994MABnf9A06ZNG+U1df+2adMmnb3+Gyh68oZek0gkmDp1Kuzs7IQuRXQaN26MU6dOqSy8BgC1atXCwYMHsXLlSoEqE49GjRph+/bt0NfXF7oUUVm4cCHq1q2rMo1aIpHghx9+QNu2bXU+r6VLlyI2NrbIifMVK1agSpUq+PLLLwWqTDycnJwQGBiI9u3bY9iwYZBKpUKXRDqK14CTzpFKpTh58iT27NmD8+fPo2nTpjo7fe3DDz98axtdzQYAoqOjMXjwYLRv3x6dOnWCtbU1kpKScPLkSZw7dw4bN27U2ZXQGzVqhClTphS7T6FQ4Ndff0VERISWqxKXixcvIjY2FnK5XGX7oEGDBKpIXJiPelu3bkW/fv1gamoqdCmioy4buVyu07OSXuHfjnpvZpOUlIQLFy6gT58+QpdFOogdcNJpSUlJ2L9/v3Ll4t9++w0ff/yxwFWJk65m8+LFC/zyyy84d+4c0tLSYGdnhzZt2mDSpElqF9nSBXPnzn1rm2XLlmmhEnGaMWMGrl69CldXV5XZAhKJBMHBwQJWJg7MR7OWLVvi/PnzMDDgUj3/xmw0Yz7qtWzZEufOnStyuR2RtrEDTvQGXb+NiSbMRjNdPUFRErqYjYeHB44fPw4bGxuhSxEl5qPZwoULkZ2djW7dusHOzk7lJIWHh4eAlQmP2WjGfNRjNiQWPD1G9Aaej1KP2Wi2evVqnetklpQuZlOvXj2kpqayg6kG89Hs3LlzAFDkpKdEIsGJEyeEKEk0mI1mzEc9ZkNiwQ440Ru4sJR6zEYznqBQTxezmT17NoYPHw5PT0+V+84Cuj01/xXmo9nJkyeFLkG0mI1mzEc9ZkNiwdUqiIhKAU9QqKeL2SxZsgQNGzaEq6srqlatqvJFzKckQkJCMGHCBPTv3x8JCQlYsmQJcnNzhS5LFJiNZsxHPWZDYsARcCIiolIWGRmJXbt26eTJh5JgPpoFBwfj4MGDGDNmDL755huYmpoiMjISixYtwrfffit0eYJiNpoxH/WYDYkFR8CJ3qCLU2VLitkQlZyPjw8OHDggdBmixXw027JlC4KDg9GvXz9IJBJYWlpi1apVOHXqlNClCY7ZaMZ81GM2JBYcASedkZWVBXNz8yLbo6KiUK1aNQBA586dtV2WKDCb/z+eoFBPF7N58uQJdu7ciaVLl8LKykplpDc0NFTAysSB+WhWUFAAY2NjAKqXcLzapsuYjWbMRz1mQ2LBDjjpjN69e2Pp0qVo3bo1AEAul2Pt2rUICAhQroi5cuVKIUsUDLPRjCco1GM2xZsxY4bQJYga89HM19cXU6ZMwfTp0yGXy3H37l2sWrUKPXr0ELo0wTEbzZiPesyGxIL3ASedcejQIfzvf/9Dt27d4Ovri6VLl8LQ0BBfffUVGjZsKHR5gmI2mnXu3PmtJyh0FbNRLzs7G6dPn0ZcXBw++OADPHz4EI0bNxa6LNFgPurJZDIEBQVh3759iIuLg729PXx8fDB16lQYGRkJXZ6gmI1mzEc9ZkNiwQ446ZSEhAQMHjwYCQkJ8PHx0elR3X9jNurxBIV6zKZ4N2/exOTJk1GzZk3cvHkTBw8eRN++fTF//nz0799f6PIEx3w0u3fvHtzc3IpsDwsLg6enpwAViQez0Yz5qMdsSCzYASedcfr0afzvf/+Dg4MDBgwYAH9/fzRs2BDz5s2Dk5OT0OUJitm8HU9QqMdsiho8eDAmT56Mzp07o0WLFrhy5QoiIiIwa9YsHD9+XOjyBMd8isrLy0NaWhoAwNvbG6Ghocr1EyQSCV6+fIlBgwYhPDxcwCqFwWw0Yz7qMRsSI14DTjrjs88+w+zZszFkyBAAQI8ePbBy5Ur4+vri2rVrAlcnLGaj2asTFFWrVsWMGTPg7++P6dOn8wQFmI06UVFR8PLyAvB6sZ9GjRohPT1dwKrEg/kUlZ2djb59+yI9PR0KhQIdOnRQ2W9oaIg+ffoIVJ2wmI1mzEc9ZkNixBFw0hkJCQlwcHAosv3GjRto0qSJABWJB7PRrEWLFionKLKzs7Fy5Urs3btX509QMJvijRkzBh07dsTo0aPh6emJsLAw7N69G3v27MHGjRuFLk9wzEezkSNHYtOmTUKXIUrMRjPmox6zIbFgB5x0SkhICPbs2YOkpCQEBgYiICAAn3/+OUxMTIQuTXDMRj2eoFCP2RQvMjISfn5+yMvLQ1xcHGrXro2srCz8+uuvqFOnjtDlCY75vN2rReri4+MxdOhQLlL3BmajGfNRj9mQGOgJXQCRtgQHByMwMBC+vr6Ijo6GqakpIiMjsWjRIqFLExyz0czBwQEhISEYP348+vfvj4SEBCxZsgSurq5ClyY4ZlO86tWr48CBA/juu+/w3XffYcGCBQgJCWHn8h/MR7ObN2+ie/fu2Lp1K1atWoWUlBSMHTsWe/bsEbo0wTEbzZiPesyGRENBpCO8vLwU8fHxCoVCoWjevLlCoVAoMjIyFJ6enkKWJQrMRrOgoCBFnz59FHv27FE0a9ZMkZGRoRgzZoxizpw5QpcmOGZTvMGDBxe7vUePHlquRJyYj2aDBg1SnDhxQqFQvH5NvnHjhqJLly5CliUKzEYz5qMesyGx4CJspDMKCgpgbGwM4PWiPwCU23QZs9Fsy5Yt2L59OxwcHLB06VJYWlpi1apV6Nq1q9ClCY7ZvBYbG4sFCxZAoVDgzp07GDt2rMr+zMxMFBQUCFSd8JhPyXGROvWYjWbMRz1mQ2LBDjjpDF9fX0yZMgXTp0+HXC7H3bt3sWrVKvTo0UPo0gTHbDTjCQr1mM1rlSpVwsiRI5GamoqrV6+id+/eKvuNjIzQvHlzgaoTHvMpuQYNGmDDhg0YPXq0ctuePXuKvYexrmE2mjEf9ZgNiQUXYSOdIZPJEBQUhH379iEuLg52dnbKjqcudhbexGw0W758OW7evInp06fDz88PGzduxKpVq+Di4oL58+cLXZ6gmE3xHj9+jFq1aim/z87OhqGhIQwNDQWsSjyYj2ZRUVGYPHlykUXqAgICULt2baHLExSz0Yz5qMdsSCzYASciegueoFCP2RTvzp07WLNmDX755Rfs378f8+bNg6mpKX788Ue0a9dO6PIEx3zerqCgABEREYiNjYW9vT2aNGkCIyMjocsSBWajGfNRj9mQGLADTv95c+fOfWubZcuWaaES8WE2RGVj+PDh8PLywrhx4+Dl5YU5c+bAxsYGS5YsweHDh4UuT3DM5+0uXryI2NhYyOVyle2DBg0SqCLxYDaaMR/1mA2JAa8Bp/+8qlWrAgCePn2K8+fPo3///qhUqRKSkpKwe/dudOjQQeAKhcNsNOMJCvWYjWaPHz/Gli1bEBERgaysLHh7e0NfXx/x8fFClyYKzEezGTNm4OrVq3B1dVVZW0Eikeh8R4HZaMZ81GM2JBbsgNN/3uTJkwEAffr0wYYNG1SuO+zfvz/Gjx8vVGmCYzaa8QSFesxGMxsbG0RERGDnzp1o164d9PX1cfz4cVSuXFno0kSB+Wh25swZHD9+HDY2NkKXIjrMRjPmox6zIbFgB5x0RkxMDBwcHFS2mZubIzU1VaCKxIPZFI8nKNRjNprNnz8fU6dORYUKFfDLL7/g0qVLmDdvHn766SehSxMF5qNZvXr1kJqayo5CMZiNZsxHPWZDYsEOOOkMX19ffPzxxxg1ahTs7e0RFxeH33//HQMHDhS6NMExG814gkI9ZlM8Y2NjnDlzRvl95cqVce7cOS728w/mo9ns2bMxfPhweHp6wsLCQmWfLl/aATCbt2E+6jEbEgsuwkY6QyaTYePGjTh+/DiSkpJgb28PHx8fDBs2TOVaIF3EbDT76quvcPfu3SInKFq1aoU5c+YIXZ6gmE3x+vTpg5SUFPTo0QM9e/bk/a3/hfloNmDAAFhbW8Pd3R36+voq+17NPtFVzEYz5qMesyGxYAeciOgteIJCPWajXmRkJEJDQxEaGoqkpCR0794dPj4+8PDwELo0UWA+6nl4eODatWs6fwwVh9loxnzUYzYkFuyAk8549OgRvvvuOzx79qzI7SdCQ0MFqkocmA1R2Tp//jyWL1+OBw8ewMnJCX379sXHH3+MChUqCF2aKDAfVQsWLICnpyf69OkjdCmiw2w0Yz7qMRsSC3bASWcMHjwY9erVQ8+ePYtMPfL09BSoKnFgNprxBIV6zEa9iIgIHDt2DMeOHUNOTo5yhNfJyQmrV6/G3bt3sWvXLqHLFAzzUW/48OG4fv06rKysYGVlpTJip+vHFbPRjPmox2xILNgBJ53h4eGBy5cvw9DQUOhSRIfZaMYTFOoxm+J5eXlBJpMpr3Fu1qyZyoe9hw8fYurUqTr7oY/5aBYWFqZ2ny4fVwCzeRvmox6zIbFgB5x0hp+fH4YMGQIvLy+hSxEdZqMZT1Cox2yKd/nyZXh6evJaQzWYz9tlZ2cjPT0drz6myWQyPH36FB07dhS4MuExG82Yj3rMhsSAtyEjnaGvr4/JkyfD1dW1yD0g161bJ1BV4sBsNGvVqhXOnz/PExTFYDbFc3Nzw4YNG/DRRx/hzp07WLx4MSpUqICFCxfCxcVF6PIEx3w027x5M7799lsUFBQAAORyOSQSCVxdXXW+o8BsNGM+6jEbEgt2wElndOnSBV26dBG6DFFiNprxBIV6zKZ4CxYsQEFBAT766CPMmzcPLVu2hIWFBebNm4eNGzcKXZ7gmI9mgYGBCAgIgEQiwYEDB/Dll19iyZIlMDc3F7o0wTEbzZiPesyGxIJT0Ok/z9PTE2FhYWjQoAEkEgn+/ScvkUhw69YtgaoTFrMpmb1796rd169fP63VIUbMpnjt2rXDqVOnEBsbCx8fH1y6dAnm5uZo1qwZrl+/LnR5gmM+mrVo0QJXrlxBWloahg8fjpCQEGRnZ6NHjx44e/as0OUJitloxnzUYzYkFhwBp/+8ffv2AQCOHj0qcCXiw2w0e3WCYv78+WpPUOhqJ5PZaCaRSJCdnY2jR4/C3d0dFhYWiIyMhKWlpdCliQLz0czFxQURERFo3LgxXr58iaSkJBgYGCA7O1vo0gTHbDRjPuoxGxILdsDpP69SpUoAAGdnZ4ErER9moxlPUKjHbDT76KOP0Lt3b7x8+RKrVq3CrVu3MH78eEycOFHo0kSB+Wg2bdo0jBs3Dnv37sWHH36IgQMHQl9fH927dxe6NMExG82Yj3rMhsSCU9CJiIjKQFRUFExNTeHg4ICUlBQ8f/4cjRs3Vu4/fPgwevbsKWCFwmI+6qWkpMDExAQmJibQ09PDtWvXkJmZiQ4dOuj8yvHMRjPmox6zIbFgB5yIiEgAHh4evN5ZA13Op23btjh69CgXhyoGs9GM+ajHbEgs9IQugIiISBfx/LdmupxPtWrVcO3aNaHLECVmoxnzUY/ZkFjwGnAiIiIBcMqjZrqcT25uLiZMmAAzMzPY2dmpZBEaGipgZcJjNpoxH/WYDYkFO+BEREREIvLFF18IXYJoMRvNmI96zIbEgh1wIiIiIhHx9PREdnY20tPTlVPxZTIZnj59KnBlwmM2mjEf9ZgNiQU74EREREQisnnzZnz77bcoKCgAAMjlckgkEri6uqJjx44CVycsZqMZ81GP2ZBYcBE2IiKiUvLJJ58AKLyF1tvUrVu3rMsRHeZTMoGBgQgICMDatWvRt29fXL9+HX379kWzZs2ELk1wzEYz5qMesyGxYAeciIiolJw5cwb37t3DvHnzkJCQgPj4+CJfr2zbtk3ASoXBfEomJycHbdu2Rf369XHjxg2YmZlh4cKFOHr0qNClCY7ZaMZ81GM2JBacgk5ERFRK+vbti/79+wMAOnToUGS/RCLB3bt3tV2WaDCfknFxcUFERAQaN26Mly9fIikpCQYGBsjOzha6NMExG82Yj3rMhsRCotDlG20SERGVgXbt2uHcuXNClyFazEezU6dOYc6cOdi7dy8OHjyIzZs3Q19fHy1btsSyZcuELk9QzEYz5qMesyGxYAeciIioDBQUFODWrVuIj4+Hra0tmjRpAgMDTjx7hflolp2dDVNTU0gkEly7dg2ZmZno0KGDTt8f/RVmoxnzUY/ZkBjwnY6IiKiUPXv2DJMmTUJWVhacnJwQGxsLAwMDBAcHo1atWkKXJzjm83ZRUVE4evQokpOT4ejoCG9vb3YS/sFsNGM+6jEbEgMuwkZERFTKFi1ahH79+uH06dPYvn07Tp8+jSFDhmDRokVClyYKzEezP//8EyNGjEBcXBxsbW3x/PlzDBs2DEeOHBG6NMExG82Yj3rMhsSCU9CJiIhKmaenJy5evAh9fX3lNplMhpYtW+LatWsCViYOzEez9u3b49dff0XDhg2V227evIkZM2bgxIkTAlYmPGajGfNRj9mQWHAEnIiIqJQ5ODggPDxcZVt4eDgqVaokUEXiwnw0k8vlqFatmsq2mjVrQiaTCVSReDAbzZiPesyGxIIj4ERERKXsxIkTmDt3Lrp06YJKlSohJiYGx48fx/Lly9GlSxehyxMc89FszZo1OHPmDPz8/ODs7IzExEQEBwejSpUq6Nu3r7Kdh4eHgFUKg9loxnzUYzYkFuyAExERlYGnT5/i2LFjiImJgYODA7y9vVGzZk3l/sTERNjb2wtYobCYj3qdO3d+axuJRKKT02aZjWbMRz1mQ2LBDjgREZEAPDw8cP36daHLEC3mo9natWsxbtw4ocsQJWajGfNRj9mQNvAacCIiIgHw/LdmzEezNWvWCF2CaDEbzZiPesyGtIEdcCIiIgHw3rOaMR/NeIJCPWajGfNRj9mQNrADTkRERFTO8ASFesxGM+ajHrMhbWAHnIiIiIiIiEgL2AEnIiIiIiIi0gJ2wImIiATAaw01Yz6aMR/1mI1mzEc9ZkPawA44ERFRGYiNjcXatWvxzTffIDMzE0eOHFHZf/bsWYEqEwfm8/9Tknsa6ypmoxnzUY/ZkDbwPuBERESl7OzZs5gzZw46duyI0NBQHD58GEOHDsXw4cMxYcIEocsTHPMp3pdffvnWNl9//bUWKhEfZqMZ81GP2ZDYGAhdABER0X/NihUrsGbNGjRt2hTHjx+Ho6Mjfv/9d4waNUqnO5ivMJ/iOTo6Cl2CaDEbzZiPesyGxIYdcCIiolKWkJCARo0aAXh9W5sqVaogLy9PyLJEg/kUb+rUqRr3v3z5UkuViA+z0Yz5qMdsSGzYASciIiplnp6e+O677zBr1izltsDAQHh4eAhYlXgwH82uXr2K77//HklJScpFoWQyGdLS0nDjxg2BqxMWs9GM+ajHbEgseA04ERFRKUtOTsbs2bNx5coVyGQyWFpaolatWvjpp584HRLM52169eqFbt26wdTUFBERERg0aBB++ukn9OrVC+PGjRO6PEExG82Yj3rMhsSCHXAiIqJSdu/ePbi6uiIxMRFxcXGws7ND5cqVhS5LNJiPZu7u7ggPD0dcXBymTp2KnTt3Ij4+HiNGjMDx48eFLk9QzEYz5qMesyGx4G3IiIiIStlHH32E3NxcODg4oHHjxuxc/gvz0czJyQkpKSlwcnLC8+fPIZVKYWtri5SUFKFLExyz0Yz5qMdsSCx4DTgREVEpa9GiBfbu3Qtvb29YW1sLXY7oMB/N+vXrh6FDh2LLli1o3749pk6dCkNDQ9SvX1/o0gTHbDRjPuoxGxILTkEnIiIqZR07dkR8fDwkEgn09fUBAAqFAhKJBLdu3RK4OuExn7cLCwtDkyZNoFAosH79emRmZmL06NGwt7cXujTBMRvNmI96zIbEgB1wIiKiUvbixQu1+5ydnbVYiTgxH81GjBiBoKAgmJubC12K6DAbzZiPesyGxIJT0ImIiEpZfHy82n3sYDKft4mLi0NWVhY7CsVgNpoxH/WYDYkFO+BERESl7LPPPlP5PiMjA9nZ2fDw8MCmTZsEqko8mI9m9erVQ+/eveHu7g47OztIJBLlvq+//lrAyoTHbDRjPuoxGxILdsCJiIhK2cmTJ4tsW7t2rcap17qE+Wjm5uYGNzc3ocsQJWajGfNRj9mQWPAacCIiIi0oKChAq1atcOXKFaFLESXmU1RKSgpevHiB+vXrQyaTwdjYWOiSRIPZaMZ81GM2JDTeB5yIiKiUxcfHq3zFxMTgt99+40q7/2A+mqWkpGDcuHHw8vLCqFGjEBUVBS8vL0RERAhdmuCYjWbMRz1mQ2LBEXAiIqJS5ubmBolEgldvsRKJBNWqVcOiRYvQqlUrgasTHvPRbOrUqahVqxamTJmCtm3b4sqVK9i2bRt27tyJnTt3Cl2eoJiNZsxHPWZDYsEOOBERkZbk5+fDyMhI6DJEi/kUatmyJc6fPw8DAwN4enoiLCwMANC8eXNcvXpV4OqExWw0Yz7qMRsSC05BJyIiKmXt2rUrsk2hUBS7XRcxH80cHBzw999/q2y7efMmKlWqJExBIsJsNGM+6jEbEguugk5ERFQKYmNj8fHHH6OgoADJycno0aOHyv7s7GxUqVJFoOqEx3xKbvbs2Zg8eTK6du2K3NxcLF26FCEhIVi8eLHQpQmO2WjGfNRjNiQWnIJORERUSu7evYuMjAxMmDABwcHBKvuMjIxQp04dmJubC1Sd8JhPyUVHRyMkJASxsbGws7ODt7c3ateuLXRZosBsNGM+6jEbEgN2wImIiEpZTk4OTE1Ni2x/+fIlLC0tBahIXJgPERHpKk5BJyIiKmW3b9/G999/j6SkJOVK3zKZDGlpabhx44bA1QmP+Wh25coVLFq0CNHR0ZDL5QAKr5GXSCS4deuWwNUJi9loxnzUYzYkFhwBJyIiKmW9evVCt27dYGpqioiICAwaNAg//fQTevXqhXHjxgldnuCYj2a9evWCr68vfH19oa+vr7LP2dlZoKrEgdloxnzUYzYkFuyAExERlTJ3d3eEh4cjLi4OU6dOxc6dOxEfH48RI0bg+PHjQpcnOOajWbNmzRAWFlakk0DM5m2Yj3rMhsSCtyEjIiIqZU5OTkhJSYGTkxOeP38OqVQKW1tbpKSkCF2aKDAfzby9vbFjxw6hyxAlZqMZ81GP2ZBYcASciIiolAUGBmLnzp3YsmULVqxYgYyMDBgaGiItLQ2bNm0SujzBMR/Nhg0bhvDwcFhaWsLGxkZlX2hoqEBViQOz0Yz5qMdsSCzYASciIioDYWFhaNKkCRQKBdavX4/MzEyMHj0a9vb2QpcmCsxHvbCwMLX7PD09tViJ+DAbzZiPesyGxIIdcCIiolI2YsQIBAUF8Z7WajCf4iUmJsLe3h7x8fFq2zg6OmqxIvFgNpoxH/WYDYkNb0NGRERUyuLi4pCVlcUOphrMp3g9evTA9evX0bFjR0gkEpV9r26XdPfuXYGqExaz0Yz5qMdsSGw4Ak5ERFTKpk6diitXrsDd3R12dnYqH/q+/vprASsTB+ZTvLCwMEgkEqj7aCaRSNCiRQstVyUOzEYz5qMesyGx4Qg4ERFRKXNzc4Obm5vQZYgW8yneqFGjYGZmpnId/JudBolEorOLRTEbzZiPesyGxIYj4ERERGUkJSUFL168QP369SGTyWBsbCx0SaLCfFQFBQXhyJEjMDAwgI+PD3r27MlrU//BbDRjPuoxGxIbdsCJiIhKWUpKCj7//HOEhYVBX18fu3btwogRIxAYGIjGjRsLXZ7gmI9m0dHRCAkJQWhoKExNTeHj44MePXrAzs5O6NIEx2w0Yz7qMRsSC3bAiYiIStnUqVNRq1YtTJkyBW3btsWVK1ewbds27Ny5Ezt37hS6PMExn5KLjIzE4cOHsX//fjg6OuL3338XuiTRYDaaMR/1mA0JSU/oAoiIiP5rrly5gmnTpsHIyEi5wNgHH3yAyMhIYQsTCeZTMomJibhw4QIuXbqElJQUTpt9A7PRjPmox2xIaFyEjYiIqJQ5ODjg77//RvPmzZXbbt68iUqVKglYlXgwH/Xi4uIQGhqK0NBQPH78GF5eXhg9ejTatm0LIyMjocsTFLPRjPmox2xITDgFnYiIqJSdOXMGn376Kbp27YpDhw5h6NChCAkJweLFi9G1a1ehyxMc8yne0KFDERkZiU6dOqFnz55o27YtDAw4VgIwm7dhPuoxGxIbdsCJiIjKwKsFf2JjY2FnZwdvb2/Url1b6LJEg/kU5ebmBj09PUgkEpV7owOFt02SSCS4deuWQNUJi9loxnzUYzYkNuyAExERlYH8/Hxcu3ZNeY1h06ZNOeryBuZT1IsXL97axtnZWQuViA+z0Yz5qMdsSGzYASciIipl4eHhmDZtGkxNTeHg4IC4uDgoFAoEBASgbt26QpcnOOZDRES6ih1wIiKiUta7d2+MHDkSQ4cOVW7bvn07du7ciT///FPAysSB+RARka7ibciIiIhK2YsXLzBgwACVbQMHDsSzZ88EqkhcmA8REekqdsCJiIhK2dChQ7F8+XJkZ2cDAKRSKVavXo2+ffsKXJk4MB8iItJVnIJORERUyjp27Ij4+Hjo6+vDysoKGRkZkMlk0NfXh0Qi0fmVd5kPERHpKnbAiYiISllJVt0FdHflXeZDRES6ih1wIiKiMnDx4kXExsZCLperbB80aJBAFYkL8yEiIl2k2zfcJCIiKgMzZszA1atX4erqColEotwukUjYwQTzISIi3cURcCIiolLm4eGB48ePw8bGRuhSRIn5EBGRruIq6ERERKWsXr16SE1NFboM0WI+RESkqzgCTkREVMr+/vtvTJw4EZ6enrCwsFDZt2zZMoGqEg/mQ0REuorXgBMREZWyJUuWoGHDhnB1dYW+vr7Q5YgO8yEiIl3FEXAiIqJS5uHhgWvXrqksMEavMR8iItJVvAaciIiolPn4+ODAgQNClyFazIeIiHQVR8CJiIhK2fDhw3H9+nVYWVnByspKZaQ3NDRUwMrEgfkQEZGuYgeciIiolIWFhand5+npqcVKxIn5EBGRruIUdCIiolLm6emJhg0bIjk5Gbdv30ajRo1gYmLCzuU/mA8REekqdsCJiIhK2c2bN9G9e3ds3boVP//8M1JSUjB27Fjs2bNH6NJEgfkQEZGuYgeciIiolC1ZsgRLlizBhg0bYGBgAGdnZ6xbtw6rV68WujRRYD5ERKSr2AEnIiIqZVFRUfDy8gIA5QJjjRo1Qnp6uoBViQfzISIiXcUOOBERUSm5du0aAKBBgwbYsGGDyr49e/bAzc1NiLJEg/kQEZGu4yroREREpcTDwwPXr19HZGQk/Pz8kJeXh7i4ONSuXRtZWVkICAhA7dq1hS5TMMyHiIh0HTvgREREpcTd3R3h4eEAgIKCAkRERCA2Nhb29vZo0qQJjIyMBK5QWMyHiIh0nYHQBRAREf1XKBQKhIeH481z205OTgCAW7duASgcBdZVzIeIiHQdR8CJiIhKSb169VC5cmWoe2uVSCQ4ceKElqsSD+ZDRES6jiPgREREpcTU1JQdSA2YDxER6Tqugk5ERFRKOKlMM+ZDRES6jh1wIiKiUtK8eXOhSxA15kNERLqO14ATERERERERaQFHwImIiIiIiIi0gB1wIiIiIiIiIi1gB5yIiIiIiIhIC9gBJyIiIiIiItICdsCJiIiIiIiItOD/AAU9wch6aTO6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df = pd.DataFrame(date)\n",
    "corr = df.corr()\n",
    "plt.figure(figsize=(16,12))\n",
    "sns.set_context('paper',font_scale=1.4)\n",
    "sns.heatmap(corr, cmap='Blues', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# Generate dataset\n",
    "X, y = make_regression(n_samples=25000, n_features=3, n_targets=2, random_state=33)\n",
    "\n",
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=33)\n",
    "\n",
    "# Create the SVR regressor\n",
    "svr = SVR(epsilon=0.2)\n",
    "\n",
    "# Create the Multioutput Regressor\n",
    "mor = MultiOutputRegressor(svr)\n",
    "\n",
    "# Train the regressor\n",
    "mor = mor.fit(X_train, y_train)\n",
    "\n",
    "# Generate predictions for testing data\n",
    "y_pred = mor.predict(X_test)\n",
    "\n",
    "# Evaluate the regressor\n",
    "mse_one = mean_squared_error(y_test[:,0], y_pred[:,0])\n",
    "mse_two = mean_squared_error(y_test[:,1], y_pred[:,1])\n",
    "print(f'MSE for first regressor: {mse_one} - second regressor: {mse_two}')\n",
    "mae_one = mean_absolute_error(y_test[:,0], y_pred[:,0])\n",
    "mae_two = mean_absolute_error(y_test[:,1], y_pred[:,1])\n",
    "print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.metrics import explained_variance_score,r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel\n",
    "\n",
    "import xgboost  as xgb\n",
    "\n",
    "# Generate dataset\n",
    "# X, y = make_regression(n_samples=25000, n_features=3, n_targets=2, random_state=33)\n",
    "name_X = [\"Temperature_of_system1\",\t\"Temperature_of_system2\",\t\"Mineral_parameter1\",\t\"Mineral_parameter2\",\t\"Mineral_parameter3\",\t\"Mineral_parameter4\"]\n",
    "name_y = [\"index_A\",\"index_B\",\"index_C\",\"index_D\"]\n",
    "# name_X = [\"index_A\",\"index_B\",\"index_C\",\"index_D\",\t\"Mineral_parameter1\",\t\"Mineral_parameter2\",\t\"Mineral_parameter3\",\t\"Mineral_parameter4\"]\n",
    "# name_y = [\"Temperature_of_system1\",\t\"Temperature_of_system2\"]\n",
    "name = [\"index_A\",\"index_B\",\"index_C\",\"index_D\",\"Temperature_of_system1\",\t\"Temperature_of_system2\",\t\"Mineral_parameter1\",\t\"Mineral_parameter2\",\t\"Mineral_parameter3\",\t\"Mineral_parameter4\"]\n",
    "X = date[name_X]\n",
    "y = date[name_y]\n",
    "\n",
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=33)\n",
    "\n",
    "# Create the SVR regressor\n",
    "# svr = SVR(epsilon=0.01,C=1.0,kernel='poly')\n",
    "# svr = SVR(epsilon=0.2,kernel='rbf')\n",
    "# svr = RandomForestRegressor(max_depth=2, random_state=0)\n",
    "# # svr = ExtraTreesRegressor(n_estimators=100, random_state=0)\n",
    "# other_params = {'learning_rate': 0.1, 'n_estimators': 300, 'max_depth': 5, 'min_child_weight': 1, 'seed': 0, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
    "# svr = xgb.XGBRegressor(objective='reg:squarederror',**other_params)\n",
    "\n",
    "# params = {\n",
    "#     'iterations':330,\n",
    "#     'learning_rate':0.1,\n",
    "#     'depth':10,\n",
    "#     'loss_function':'RMSE'\n",
    "\n",
    "# }\n",
    "\n",
    "\n",
    "# svr = CatBoostRegressor(**params)\n",
    "\n",
    "\n",
    "# Create the Multioutput Regressor\n",
    "# mor = MultiOutputRegressor(svr)\n",
    "\n",
    "\n",
    "svr1 = SVR(epsilon=0.2,kernel='rbf')\n",
    "# other_params = {'learning_rate': 0.1, 'n_estimators': 300, 'max_depth': 5, 'min_child_weight': 1, 'seed': 0, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
    "# svr2 = xgb.XGBRegressor(objective='reg:squarederror',**other_params)\n",
    "svr3 = RandomForestRegressor(max_depth=2, random_state=0)\n",
    "# kernel = DotProduct() + WhiteKernel()\n",
    "# svr4 = GaussianProcessRegressor(kernel=kernel,random_state=0)\n",
    "# params = {\n",
    "#     'iterations':330,\n",
    "#     'learning_rate':0.1,\n",
    "#     'depth':10,\n",
    "#     'loss_function':'RMSE'\n",
    "\n",
    "# }\n",
    "\n",
    "\n",
    "# svr4 = CatBoostRegressor(**params)\n",
    "\n",
    "# models = list()\n",
    "# models.append(('xg', MultiOutputRegressor(svr2)))\n",
    "# models.append(('svr', MultiOutputRegressor(svr1)))\n",
    "# models.append(('RFR', MultiOutputRegressor(svr3)))\n",
    "\n",
    "models = list()\n",
    "# models.append(('xg', svr2))\n",
    "models.append(('svr', svr1))\n",
    "models.append(('RFR', svr3))\n",
    "# models.append(('cat', svr4))\n",
    "# models.append(('GaussianProcessRegressor', svr4))\n",
    "    # define the voting ensemble\n",
    "svr = VotingRegressor(estimators=models)\n",
    "\n",
    "mor = MultiOutputRegressor(svr)\n",
    "\n",
    "\n",
    "# # Train the regressor\n",
    "mor = mor.fit(X_train, y_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:\n",
      "1  0.4636767508663284\n",
      "2  1.2383898819401873\n",
      "3  0.5667349440793973\n",
      "4  5.345592506550024\n",
      "MAE:\n",
      "1  0.5059470329337451\n",
      "2  0.8797094476711043\n",
      "3  0.5739154881267753\n",
      "4  1.9259426188850235\n",
      "可解释的方差分数:\n",
      "1  0.34516966381851666\n",
      "2  0.22326886843187665\n",
      "3  0.31954438041519184\n",
      "4  0.2128703989941979\n",
      "r2_score:\n",
      "1  0.34516926029176476\n",
      "2  0.22280206894925303\n",
      "3  0.3099388848627619\n",
      "4  0.2022681136789063\n",
      "mean_absolute_percentage_error:\n",
      "1  0.006307551009458884\n",
      "2  0.03815529518247527\n",
      "3  0.04820764125574418\n",
      "4  0.11666647730842294\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions for testing data\n",
    "y_pred = mor.predict(X_test)\n",
    "# Evaluate the regressor\n",
    "y_test = y_test.values\n",
    "# y_test\n",
    "mse1 = mean_squared_error(y_test[:,0], y_pred[:,0])\n",
    "mse2 = mean_squared_error(y_test[:,1], y_pred[:,1])\n",
    "mse3 = mean_squared_error(y_test[:,2], y_pred[:,2])\n",
    "mse4 = mean_squared_error(y_test[:,3], y_pred[:,3])\n",
    "# print(f'MSE for first regressor: {mse_one} -second regressor: {mse_two}')\n",
    "print(\"MSE:\")\n",
    "print(\"1  \" + str(mse1))\n",
    "print(\"2  \" + str(mse2))\n",
    "print(\"3  \" + str(mse3))\n",
    "print(\"4  \" + str(mse4))\n",
    "\n",
    "mse1 = mean_absolute_error(y_test[:,0], y_pred[:,0])\n",
    "mse2 = mean_absolute_error(y_test[:,1], y_pred[:,1])\n",
    "mse3 = mean_absolute_error(y_test[:,2], y_pred[:,2])\n",
    "mse4 = mean_absolute_error(y_test[:,3], y_pred[:,3])\n",
    "# print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')\n",
    "print(\"MAE:\")\n",
    "print(\"1  \" + str(mse1))\n",
    "print(\"2  \" + str(mse2))\n",
    "print(\"3  \" + str(mse3))\n",
    "print(\"4  \" + str(mse4))\n",
    "\n",
    "mse1 = explained_variance_score(y_test[:,0], y_pred[:,0])\n",
    "mse2 = explained_variance_score(y_test[:,1], y_pred[:,1])\n",
    "mse3 = explained_variance_score(y_test[:,2], y_pred[:,2])\n",
    "mse4 = explained_variance_score(y_test[:,3], y_pred[:,3])\n",
    "# print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')\n",
    "print(\"可解释的方差分数:\")\n",
    "print(\"1  \" + str(mse1))\n",
    "print(\"2  \" + str(mse2))\n",
    "print(\"3  \" + str(mse3))\n",
    "print(\"4  \" + str(mse4))\n",
    "\n",
    "\n",
    "mse1 = r2_score(y_test[:,0], y_pred[:,0])\n",
    "mse2 = r2_score(y_test[:,1], y_pred[:,1])\n",
    "mse3 = r2_score(y_test[:,2], y_pred[:,2])\n",
    "mse4 = r2_score(y_test[:,3], y_pred[:,3])\n",
    "# print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')\n",
    "print(\"r2_score:\")\n",
    "print(\"1  \" + str(mse1))\n",
    "print(\"2  \" + str(mse2))\n",
    "print(\"3  \" + str(mse3))\n",
    "print(\"4  \" + str(mse4))\n",
    "\n",
    "mse1 = mean_absolute_percentage_error(y_test[:,0], y_pred[:,0])\n",
    "mse2 = mean_absolute_percentage_error(y_test[:,1], y_pred[:,1])\n",
    "mse3 = mean_absolute_percentage_error(y_test[:,2], y_pred[:,2])\n",
    "mse4 = mean_absolute_percentage_error(y_test[:,3], y_pred[:,3])\n",
    "# print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')\n",
    "print(\"mean_absolute_percentage_error:\")\n",
    "print(\"1  \" + str(mse1))\n",
    "print(\"2  \" + str(mse2))\n",
    "print(\"3  \" + str(mse3))\n",
    "print(\"4  \" + str(mse4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[79.91343823, 23.28376744, 11.65421834, 15.86865318],\n",
       "       [80.15569834, 23.02861803, 11.37268505, 16.70888928],\n",
       "       [79.68830702, 23.22460312, 11.78011087, 17.59789173],\n",
       "       [80.35055196, 22.5839979 , 11.52425262, 16.13783777],\n",
       "       [79.53794079, 23.52641059, 12.22477214, 15.69024037],\n",
       "       [79.46257038, 23.77951416, 12.3056632 , 16.46344207],\n",
       "       [79.45607784, 23.78911958, 12.33806761, 16.44025886],\n",
       "       [80.13433928, 23.04133962, 11.27151676, 16.11870653],\n",
       "       [79.71880552, 23.23613274, 11.60460722, 20.09491584],\n",
       "       [79.55955921, 23.53636986, 12.09317419, 15.79477867],\n",
       "       [79.43823843, 23.8179177 , 12.36123922, 16.44354613],\n",
       "       [79.44981196, 23.61429143, 12.18065017, 15.64652162],\n",
       "       [79.85931741, 23.27863853, 11.74293128, 16.0755257 ],\n",
       "       [79.4417592 , 23.80120922, 12.34997035, 16.44019802],\n",
       "       [79.43891562, 23.61733905, 12.18833977, 15.6660495 ],\n",
       "       [80.14156667, 23.0523216 , 11.3322929 , 17.21211189],\n",
       "       [79.49453153, 23.54113355, 12.07754978, 15.85145679],\n",
       "       [79.43290602, 23.6164969 , 12.19179632, 15.68255002],\n",
       "       [80.14844832, 23.05314748, 11.33402057, 17.20725214],\n",
       "       [79.82614866, 23.10737121, 12.17827411, 16.1786655 ],\n",
       "       [80.19190659, 23.05548511, 11.33426678, 17.14853371],\n",
       "       [79.50996666, 23.59190379, 12.17247159, 15.6894794 ],\n",
       "       [80.45682638, 22.62381544, 11.98820031, 17.08219842],\n",
       "       [80.13425585, 23.05041034, 11.27703658, 16.11864654],\n",
       "       [80.18004946, 23.05536605, 11.35144944, 17.15899807],\n",
       "       [80.18005863, 23.05535185, 11.35142778, 17.15904171],\n",
       "       [79.83053282, 23.54404915, 12.00076911, 17.3743386 ],\n",
       "       [80.18006615, 23.05535038, 11.35143043, 17.15901906],\n",
       "       [80.19200272, 23.05538815, 11.35148313, 17.15893018],\n",
       "       [79.52746467, 23.58866116, 12.16557863, 15.68045768],\n",
       "       [79.52535452, 23.58187633, 12.16723587, 15.68111363],\n",
       "       [80.52006747, 22.44199058, 11.28175095, 16.4354199 ],\n",
       "       [80.13433965, 23.05032931, 11.27693875, 16.11876136],\n",
       "       [80.09709472, 23.14167297, 11.24458173, 16.13186457],\n",
       "       [80.15557997, 23.02870696, 11.37945846, 16.70886792],\n",
       "       [80.13950089, 23.05365682, 11.28505982, 16.16407496],\n",
       "       [80.45704086, 22.62358589, 11.98790989, 17.08261404],\n",
       "       [79.68825852, 23.22466456, 11.78019095, 17.59777932],\n",
       "       [79.4837852 , 23.55372832, 12.09638589, 15.82680234],\n",
       "       [80.45689454, 22.62373577, 11.98809562, 17.08236747],\n",
       "       [80.18001375, 23.05539647, 11.35148386, 17.15897107],\n",
       "       [80.18018297, 23.05523311, 11.35128216, 17.15920952],\n",
       "       [80.18003382, 23.05537953, 11.35146483, 17.15898536],\n",
       "       [80.10221934, 23.14159571, 11.24705986, 16.17710961],\n",
       "       [79.53064534, 23.71081311, 12.21664866, 16.55066032],\n",
       "       [80.14162748, 23.05227242, 11.33223932, 17.21214323],\n",
       "       [80.13955197, 23.0536059 , 11.28725753, 16.19092611],\n",
       "       [80.18008882, 23.05532449, 11.35139537, 17.159074  ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>index_A</th>\n",
       "      <th>index_B</th>\n",
       "      <th>index_C</th>\n",
       "      <th>index_D</th>\n",
       "      <th>Temperature_of_system1</th>\n",
       "      <th>Temperature_of_system2</th>\n",
       "      <th>Mineral_parameter1</th>\n",
       "      <th>Mineral_parameter2</th>\n",
       "      <th>Mineral_parameter3</th>\n",
       "      <th>Mineral_parameter4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1404.89</td>\n",
       "      <td>859.77</td>\n",
       "      <td>52.75</td>\n",
       "      <td>96.87</td>\n",
       "      <td>46.61</td>\n",
       "      <td>22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151.75</td>\n",
       "      <td>859.77</td>\n",
       "      <td>52.75</td>\n",
       "      <td>96.87</td>\n",
       "      <td>46.61</td>\n",
       "      <td>22.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time  index_A  index_B  index_C  index_D  Temperature_of_system1  \\\n",
       "0 2022-01-23      NaN      NaN      NaN      NaN                 1404.89   \n",
       "1        NaT      NaN      NaN      NaN      NaN                 1151.75   \n",
       "\n",
       "   Temperature_of_system2  Mineral_parameter1  Mineral_parameter2  \\\n",
       "0                  859.77               52.75               96.87   \n",
       "1                  859.77               52.75               96.87   \n",
       "\n",
       "   Mineral_parameter3  Mineral_parameter4  \n",
       "0               46.61               22.91  \n",
       "1               46.61               22.91  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = pd.read_excel(\"preid.xlsx\")\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[80.11728996, 23.12142028, 11.34589069, 16.68030346],\n",
       "       [79.81495678, 23.39226235, 11.67464471, 15.88185958]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_val = mor.predict(pr[name_X])\n",
    "pt_val"
   ]
  }
 ],
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